The Next Tech Bubble - Lessons from the Dot-Com Crisis for Today’S Investors

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today’s Investors

Benedikt Glatzl

A dissertation submitted to the University of Dublin in partial fulfilment of the requirements for the degree of MSc in Management of Information Systems

7th of September 2016

Declaration

I declare that the work described in this dissertation is, except where otherwise stated, entirely my own work, and has not been submitted as an exercise for a degree at this or any other university. I further declare that this research has been carried out in full compliance with the ethical research requirements of the School of Computer Science and Statistics.

Signed: Benedikt Glatzl 7th of September 2016

Permission to lend and/or copy

I agree that the School of Computer Science and Statistics, Trinity College may lend or copy this dissertation upon request.

Signed: Benedikt Glatzl 7th of September 2016

Acknowledgements

I would like to thank my supervisor Dr Denise Leahy for guiding me through the research process with empathy and patience and teaching me to never compromise on good academic standards even when faced with tough deadlines.

I also want to thank the twelve individuals who agreed to be interviewed, accommodated my interview requests despite their very busy schedules and were very open when giving insights into the tech investment industry. Their input was vital for this research and I am incredibly grateful for their efforts.

I also want to thank my family for teaching me the value of academic achievement, my university classmates for creating a cooperative, friendly and fun learning environment and my friends and co-workers for giving me the time and space I needed for finishing this dissertation.

Last, but not least, my special thanks go to Nicole, who supported me throughout the ups and downs of the past years with love, understanding and great humour. Without her I would not have managed to get where I am today.

iii

Abstract

In recent years, investments in technology start-ups experienced a steep rise in number and volume with the valuation of some tech companies rising above 1 billion USD within just a few years from their founding. The high sums being poured into the tech market and the quick rise of tech companies' valuations are reminiscent of the dot-com crisis of the late 1990s where the market for internet-based ventures showed a similarly stellar increase in volume, but eventually deflated causing a global recession. However, it is unclear if investors are behaving in a way that would once again lead to a tech bubble or if their strategies have improved and adapted to the new situation.

The purpose of this research is to understand factors institutional and private tech investors take into consideration in the process of evaluating and deciding on a potential investment in the tech sector and how these compare to investment factors during the dot- com bubble time period. It also aims to deepen the understanding on how such behaviours could indicate the build-up of a tech investment bubble in the current situation.

For the purpose of this study twelve individuals from eight countries working as private or institutional investors were interviewed. Key themes identified in the interviews were further developed and contrasted with findings from previous research on the dot-com bubble time period.

This study shows that some aspects of today’s tech investment strategies improved (e.g. focus on validating business models, improved strategies for acquiring market knowledge) while some mistakes from the dot-com era are being repeated (e.g. focus on revenue development rather than profitability, unclear exit goals). Furthermore, the study identifies indicators for a generally overvalued tech market like high pressure to invest and irrational valuations of so-called unicorn companies.

Keywords: management, venture capital, entrepreneurship, tech start-ups, technology investment, dot-com bubble, financial crisis

Table of Contents Chapter 1: Introduction ...... 1 1.1 Research Context & Background ...... 1 1.2 Research Question ...... 2 1.3 Importance & Relevance ...... 3 1.4 Scope of the Study ...... 3 1.5 Roadmap of subsequent Chapters ...... 4 Chapter 2: Literature Review ...... 5 2.1 Introduction ...... 5 2.2 Context ...... 5 2.3 Financial Performance Indicators considered in Investment Decisions ...... 9 2.4 Non-financial Factors considered for Investment Decisions ...... 11 2.5 Other Factors that impacted Investment Decisions ...... 14 2.6 Developments since the Dot-com Crisis ...... 16 2.7 Summary ...... 16 2.8 Open Questions ...... 17 2.9 Positioning the Research Question ...... 18 Chapter 3: Research Methodologies ...... 19 3.1 Introduction ...... 19 3.2 Research Objective ...... 19 3.3 Research Philosophy ...... 19 3.4 Research Strategy ...... 22 3.6 Research Process ...... 24 Chapter 4: Findings ...... 27 4.1 How the Research was conducted and analysed ...... 27 4.2 Overview on Participants ...... 28 4.3 Findings on relevant Investor Knowledge ...... 28 4.4 Findings on financial Factors taken into Consideration ...... 33 4.5 Findings on non-financial Factors taken into Consideration ...... 39 4.6 Findings on the Founder Team ...... 40 4.7 Findings on Decision-making & Learning Processes of Investors ...... 45 4.8 Findings on Market Bubble & Market Hype ...... 47 4.9 General Findings ...... 54 Chapter 5: Conclusions ...... 56 5.1 Introduction ...... 56 5.2 Findings Summary ...... 56 5.3 Answering the Research Questions ...... 56 5.4 Summary of Conclusions ...... 60 5.5 Relevance of Research Question ...... 61 5.6 Limitations of Research ...... 61 5.7 Recommendations for Future Research ...... 63 5.8 Closing Remarks ...... 63 References...... 65 Appendices...... 71

Table of Figures

FIGURE 2.1 - NASDAQ index values from October 1998 till May 2002...... 7

FIGURE 2.2 - Street.com Index values 1998 till 2002 ...... 8

FIGURE 4.1 - Organisational Setup & Investment Volumes...... 28

FIGURE 4.2 - Educational & Professional Backgrounds of Interviewed Investors...... 29

List of Tables TABLE 3.1 - Comparison of institutional investors and angel investors...... 24

TABLE 3.2 - Overview of Study Participants...... 26

TABLE 4.1 - Ratings of the Importance of Knowledge and Knowledge Self-evaluation.....32

TABLE 4.2 - Considered Financial Factors...... 34

TABLE 4.3 - Newly Mentioned Financial Factors...... 34

TABLE 4.4 - Importance of Pointed out Factors Impacting the Financial Model...... 38

TABLE 4.5 - Considered Non-Financial Factors...... 39

TABLE 4.6 - Newly Mentioned Considered Non-Financial Factors...... 39

TABLE 4.7 - Considered Factors in regards to the Founder Team...... 42

TABLE 4.8 - Newly Mentioned Considered Factors in regards to the Founder Team...... 42

TABLE 4.9 - Methods for Improving Decision Making & Learning...... 47

TABLE 4.10 - Answers on the Question "Do most investors fail in valuations?" ...... 48

TABLE 4.11 - Answers to the Question "Is there a tech investment bubble right now?".. ..49

TABLE 4.12 - Parallels to the dot-com bubble...... 50

TABLE 4.13 - Differences to the dot-com bubble...... 51

TABLE 4.14 - Answers on the Question "Are current unicorn companies overvalued?"... .52

TABLE 4.15 - Lessons from the dot-com crisis...... 53

vii

Abbreviations

B2B Business to Business bn billion

CAC Cost of Acquisition

CV Curriculum Vitae

DCF Discounted Cash Flow

EU European Union

ICT Information and Communications Technology

IPO Initial Public Offering

IS Information Systems

IT Information Technology

KPI Key Performance Indicator

LTV Lifetime Value

NASDAQ National Association of Securities Dealers Automated Quotations

P/E Price to Earnings Ratio

P/S Price to Sales Ratio

R&D Research and Development

US United States

USD United States Dollar(s)

VC Venture Capitalist

viii The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 1 September 2016

Chapter 1: Introduction

1.1 Research Context & Background

When access to the World Wide Web started spreading amongst the general population in the late 1990s, many entrepreneurs saw an opportunity to tap into new markets and create new business models based on this emerging base technology. The financial market actors soon noticed the potential of such companies and as public excitement about the possibilities of the internet grew, so did investment volumes. When the internet- based company “Yahoo!” made its initial public offering (IPO) in April 1996, the price of its stock rose from $13 to $33 within a single day, more than doubling the worth of the company on the financial markets (Press, 2016). Many other young tech companies would soon follow and with each new tech investment round and IPO, the financial market volume would increase. However, a few years later in March 2000, within only a month, the NASDAQ would lose nearly a trillion dollars and in the consecutive months many of the formerly celebrated tech companies would go bankrupt and take large sums of invested money with them (Geier, 2015).

This time period was later named the “dot-com crisis” and had a profound impact on the tech industry and tech investors for many years. In the aftermath of the tech bubble burst, investments into tech companies plummeted (Howcroft, 2001) and the “irrational exuberance” of the dot-com era (Shiller, 2005) was replaced by a general weariness and caution towards the promises of new technologies and suspicion towards the financial long-term viability of start-ups in the tech area. It took years for the tech industry and the financial markets to recover (Ning et. al., 2014). However, memories of the dot-com bubble build up and eventual implosion resurfaced in recent years as tech investments both took up speed and increased in volume once again (see Mims, 2016). Also, as a rising number of only recently founded companies crossed the one billion US dollar valuation line in shorter and shorter time business models are again being scrutinized and judged to be unclear or even questionable (see Lashinsky, 2015). Economists started drawing parallels between the current financial tech markets and the situation in the late 1990s and are calling for increased caution (Curwen, 2016). Tech investors are being examined more carefully for the fear that history might repeat itself and another crisis might be in the making. Hence, the question arose, if investors learned anything from the mistakes that were done during the dot-com era or if they might unintentionally be steering the tech market towards yet another bubble.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 2 September 2016

Much research has been done on mistakes unsuccessful tech investors made in their evaluations of tech start-ups during the dot-com bubble time period, how these could have been avoided and how tech start-up valuation & investment strategies could be improved. Fields of improvement included the evaluation of financial development (Schwartz and Moon, 2000), the product of the IT start-up (Romanova et. al., 2012) and soft factors like the capabilities of the founder team (Hudson and Evans, 2005) and the way investors build and maintain assumptions about all of these factors (DaSilva and Trkman, 2013). However, only little and incidental research was carried out on how the situation in recent years compares to the dot-com crisis and there is only little research tech investors and tech entrepreneurs can turn to when trying to improve their strategies for evaluating tech companies (Valliere and Peterson, 2004).

1.2 Research Question

The purpose of this research is to deepen the understanding of tech start-up investment strategies of professional investors and if and how they adapted their evaluation models and investment strategies to the lessons from the dot-com crisis period (1997-2001). While research on the subject has been mainly limited to quantitative analyses of market movements and individual valuations and makes little or no reference and comparison to the dot-com bubble time period, this dissertation aims to examine the decision models of investors and factors being taken into account for tech start-up investments in the current financial market and to compare them directly to what was identified as relevant in this field by researchers examining the dot-com crisis. Recommendations on the factors that led to the dot-com crisis and considerations that could have prevented investment decisions with a negative outcome before and during the crisis will be directly related to the findings of this study and conclusions drawn accordingly.

The primary research question of this study is: Parallels and differences to the dot-com bubble - are there indications of investor behaviour leading to another tech market crash?

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 3 September 2016

The corresponding sub-questions are: 1) In which aspects do tech start-up investors follow lessons and recommendations from the research on the dot-com crisis in their investment strategies in 2016? 2) Which mistakes done during the dot-com bubble period are tech start-up investors repeating in 2016? 3) Are there indications that the behaviours of investors could steer the tech investment market towards another financial crash?

1.3 Importance & Relevance

This research is relevant to a number of groups within the tech community and financial industry. Founders and executives of young tech companies should be able to use this research to deepen their understanding of priorities of investors and how those evolved since the dot-com crisis and use this for improving their efforts to raise investments for their companies. Investors in the tech industry will find this research useful for examining their own investment strategies, checking for mistakes in assumptions and evaluations and improving their investment models and processes for increased return and lower risk. Furthermore, financial analysts and economists can use the insights of this research to gain impulses for their own analysis of the current situation on the financial markets specifically in regards to tech companies and improve the quality of their own research.

1.4 Scope of the Study

This study investigates current venture capital investment strategies in the tech industry from a holistic perspective taking into consideration hard quantifiable and soft qualitative factors in regards to evaluated start-ups and the attitudes and mindsets of the investors themselves and compares them to factors identified as relevant for successful or failed investments in the aftermath of the dot-com crisis. In order to achieve this, interviews were conducted with 12 individuals working in institutional venture capital funds or as angel investors. According to Ibrahim and Rogers (2008), angel investors are

“wealthy individuals who personally finance the same high-risk, high-growth start- ups as venture capitalists but at an earlier stage.” (Ibrahim and Rogers 2008)

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 4 September 2016

The participants were selected to ensure a variety in geographic focus, investment volumes and stages of investments. The outcomes were then compared to the findings on investment strategies during the dot-com bubble period and conclusions drawn based on this comparison.

1.5 Roadmap of subsequent Chapters

Chapter 1 provides context and background information on the research, presents the research questions and its relevance

Chapter 2 gives an overview of the literature relevant to the research and positions the research question in the context of the literature

Chapter 3 explains considered methodologies, justifies the choice of using interviews to gather data and outlines the steps taken to answer the research question

Chapter 4 describes the process of analysing the gathered data and presents the findings drawn from it

Chapter 5 closes the research by presenting conclusions, limitations and future research opportunities

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 5 September 2016

Chapter 2: Literature Review

2.1 Introduction

In this chapter, relevant literature on technology venture valuation during the dot-com bubble period (which is usually placed in the timeframe of 1997 to 2001) and thereafter is reviewed. First, the context is established on what a financial bubble and specifically the dot-com bubble is and how the basic mechanism underlying stock pricing, and hence bubble building works. Once this context is examined, a summary of literature covering the most important models and factors that were identified as underlying the mechanisms of the dot-com bubble is given: ● The financial factors used for evaluating tech start-ups during the dot-com bubble time period ● Non-Financial factors that were factored into the evaluation of tech start-ups during dot-com bubble time period ● Other factors that contributed to a misevaluation of start-ups by venture capital investors After that, a brief overview is given on how tech start-up valuation has changed since the dot-com crisis. Finally, conclusions and research suggestions pointed out will be summarized to provide a basis for the following chapter.

2.2 Context

2.2.1 Stock Market Bubbles

Eatwell et. al. (1987) states that a stock market bubble is characterized by a rapid increase in asset (in this case that means stock) pricing; an initial price hike creates expectations towards further increasing prices, which leads to speculative investors entering the investment market for an asset, who are more interested in trade profits rather than the capacity of a company to be actually sustainable. This means that in such a situation the price increase is a deviation from the real earnings potential of a company (Leone and Medeiros, 2015) and this deviation additionally fuelled by investors looking to “ride” the bubble, i.e. recognizing the bubble and trying to generate profit for themselves by selling these stocks at an even higher price (Zeng, 2016). Rosser (2000) specifies further what would be factors that could be considered valid for an evaluation of the real earnings potential and thus asset pricing, as this would help identify when a rapid

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 6 September 2016

sustained increase in stock prices can be considered a deviation and hence a bubble building. He did this by linking it to “market fundamentals” - factors that can be used to determine a realistic stock price. He comes to the conclusion that one can talk about a bubble in situations where pricing of an asset is not corresponding to its market fundamentals for a prolonged time and where this is unrelated to random events and shocks.

2.2.2 Pricing of Stocks

The underlying mechanism of the stock markets and the building of a stock market bubble is the pricing of stocks. Siegel (2003), in his study of price bubbles, notes that the two essential factors for arriving at a stock price are the expectations about the level of future cash flows and the time-frame in which these cash flows can be realized. This approach constitutes the “Discounted Cash Flow” (DCF) method, which calculates the value of an asset today by calculating (discounting) what future cash flows would be worth today. Siegel pinpoints the fundamental principle of any pricing decision to setting the expectations right based on “reasonable” assumptions and notes that exactly this is the fundamental challenge of any financial evaluation as “reasonable” could mean many different things depending on the model that is applied for the evaluation. Furthermore, he states that even though investment decisions can be considered rational given the expectations and the assumptions used to calculate the price of a stock, these assumptions might still be simply plain wrong.

2.2.3 The dot-com Bubble

In the centre of the dot-com bubble were publicly traded internet-based companies that experienced a rapid inflation and deflation in valuation between 1997 and 2001 (Valliere & Peterson, 2004). The name is derived from the domain ending “.com”, which the newly established Internet businesses most commonly used for their websites at that time. Shiller (2005) comments that the spread of the internet as a new base technology amongst consumers promised the creation of and access to new markets for companies and it was widely assumed that the internet was ushering in a new technological era in business and society. He notes that this thinking created a general sense of euphoria about this new era with supposedly unprecedented growth opportunities and an atmosphere of relative carelessness amongst investors, whose investment behaviour

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 7 September 2016

became more risk-taking in nature as a consequence of the positive outlook on growth opportunities.

Due to the dot-com bubble and the massive amounts of investments in this market, stock markets around the world would experience rapid increases in their total value and then crash when the bubble deflated. Figure 2.1 shows a graph of the development of the NASDAQ index at the time and illustrates the rapid price increase starting in the second half of 1998 and the peak on March 10th in the year 2000 with the NASDAQ Composite index closing at its peak at 5048 points - a level it would not reach again until April 2014 (Google Finance, 2016).

FIGURE 2.1 - NASDAQ index values from October 1998 till May 2002 (Google Finance, 2016)

During the peak time of the tech stock market rally (between 1996-2000), the stock prices of information technology providers increased by more than 35 times compared to the value they had before the start of the rally (Hendershott, 2014). While Hendershott relies on numerical multipliers to illustrate the magnitude of the bubble, Siegel (2003) uses the “Street.com” internet index, which was composed of 20 large active internet stocks, to

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 8 September 2016

show the development of the bubble even more drastically (see figure 2.2).

FIGURE 2.2 - Street.com Index values 1998 till 2002 (Siegel, 2003)

Siegel (2003) also states that the unusually high prices of stocks at the time could, given the large potential of new technologies, have in fact been justified and seemingly overvalued companies still could have had the potential to generate corresponding cash flows some time in the future. However, Siegel notes, as the real future cash flows can only be known after such a massive price increase occurred, according to him it is impossible to know right away during a price rally, if it is truly justified or if a bubble is building. Partially confirming Siegel’s value justification hypothesis, a later analysis by Hendershott (2014) of the overall value that was invested and created due to the investments in internet companies from the year 1997 to 2000 showed that overall there was positive value created by dot-com companies. He notes that 21 billion US dollars were invested by private and public investors in that timeframe leading to a liquid value of 38.7 billion US dollars towards the end of 2001. He concludes that the increase in stock prices was therefore not entirely irrational, but still overall a rather inefficient allocation of investments.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 9 September 2016

2.2.4 Framework for the Literature Review

The next chapters the research on the root causes of the dot-com is examined and an overview of the most important findings given. The structure of these chapters is loosely oriented towards the one introduced by Khanin et. al. (2008). In the analysis of the literature on venture capitalists’ decision criteria, Kahnin et. al. found that researchers before, during and after the dot-com bubble have differing opinions about which are the most prevalent criteria for investors. They conclude that researchers are split between those that argue that soft criteria like the capabilities of the management team are most important and others that argue that measurable financial and non-financial company performance indicators matter the most. However they did not specifically analyse literature on the dot-com bubble times, hence in the following sections, the research on this time period is analysed using their framework.

2.3 Financial Performance Indicators considered in Investment Decisions

In the centre of all efforts to evaluate potential investments are the financial Performance indicators as they are most closely tied to the expected future value and cash flows of a company. This is confirmed by Gavious & Schwartz (2011) in an analysis of investors' adherence to classical accounting principles before, during and after the dot-com bubble. It is furthermore pointed out that traditionally earnings and the value of assets according to bookkeeping are used to evaluate a company’s future earnings potential. The research also found that such assets are not easy to evaluate for most start-up firms in the tech sector, as they are often intangible. Additionally, these companies are often at an early stage when seeking investors, so there are little revenue and usually negative earnings. The research notes that this makes it hard to give a valuation to these companies when trying to apply generally accepted accounting principles used for evaluating old economy businesses. The analysis shows that while investors did adhere to classical accounting principles and valuations of companies before the dot-com bubble correlated with book value and current earnings, they departed from them during the bubble period.

Ofek and Richardson (2002) mentions that some investors tried to make the “earnings” indicator and hence the discounted cash flow (DCF) method usable by assuming profit margins of comparable “old economy” companies as a proxy of the margins a mature IT company might achieve. However for the prices during the dot-com bubble years to be justified, this would mean such investors expected earnings growth of 40.6% each year for

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 10 September 2016

10 years to achieve the target stock price / earning ratio that those prices implied. Ofek and Richardson suggests using earnings of comparable old economy companies as a good reality check for investments, but notes that investors did seem to accept the implied stellar growth rates anyway and went ahead with investing even when the bubble was already building. It is further elaborated on the expectations of investors and found that investors of software companies expected costs to rise only by a negligible amount when scaling the business (due to the assumption of low distribution and low additional product development costs). Ofek and Richardson state that investors expected that once an IT company entered profitability, the profit margins would increase further still rather than get smaller, which at the time outside of the tech investment market was a very unorthodox assumption and as a matter of fact did turn out to be wrong for most internet companies in the end.

While the Price to Earnings (P/E) ratio is the classical measure of assessing the validity of stock prices, Schwartz and Moon (2000) argue that it is not adequate for stocks of young tech companies. They encompass this criticism in a more sophisticated model based on real-options theory for evaluating a potential investment in the tech sector and come to the conclusion that while this is giving a more accurate picture of a company’s worth, it introduces additional uncertainty in the necessary estimates of revenue changes and the growth rate of revenues. Because of this uncertainty, parameters in their new model require the ability to build an informed opinion in order to be estimated properly. They consider this part of the modelling process as vital and the most important one of the analysis process. They state that it requires ample knowledge of the particular business that is evaluated and the industry it is operating in, as the companies have little historic data, which could be used as a basis for estimations.

Brunnermeier and Nagel (2004) on the other hand showed that while many investors tried to figure out if prices were justified using the classical indicators like Price to Earnings ratios, sophisticated investors like hedge funds concentrated on an investment strategy of evaluating Price to Sales (P/S) ratios and growth rates in the prices over the trailing 12 months period. Griffin et. al. (2011) confirm this and adds that an unusually high Price to Sales ratio seems to be a better indicator for a bubble building than high price to earnings ratios.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 11 September 2016

2.4 Non-financial Factors considered for Investment Decisions

2.4.1 Product & Business Model DaSilva and Trkman (2013) examines the use of the word “business model” and found that it only started being used frequently in both general and scientific literature during the dot-com bubble times. The paper states that because the new, upcoming technologies brought up new ways of capturing financial value, new start-ups tried to convey this by explaining new and different ways to make money in their business plans. According to DaSilve and Trkman, it encompassed key assumptions about the market and the (potential) customers. However, investors latched onto the proposed business models too quickly. DaSilva and Trkman also mentions that the main problems at the time were that assumptions the business models were based on were flawed and that there was often a fundamental misunderstanding about the difference between value creation (what users get out of a service) and value capture (how the service is monetized). It is furthermore shown that these flawed assumptions did lead to the demise of start-ups on a fundamental basis and that businesses that only pitched how they create value and not how to monetize this value failed when it became necessary to turn into a sustainable business.

The importance of understanding the business model of a tech startup is underlined by Romanova et. al. (2012), who shows that a major mistake of investors during the dot-com bubble was to assign valuations to start-ups without getting to know how the companies were intending to generate profits and without understanding the product portfolio and how viable the products actually were. It is also mentioned that ignorance and sloppy behaviour of the investors was one of the main reasons for inflated valuations of the stocks of the dot-com companies.

2.4.2 Technological Setup While most young tech ventures do not have a high level of research and development expenditures, for those that do there is a positive correlation in the increase in the value of the company and the spending on research and development for new products/services (Gollotto and Sungsoo, 2004). However, Gollotto and Sungsoo note that this insight is only of limited value for venture capitalists, as tech ventures in their early stages rarely have a dedicated R&D unit or designated R&D expenditures beyond regular product development.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 12 September 2016

Based on an examination of start-up failure rates, Cusumano (2013) recommends that investors should put greater focus on examining the flexibility of start-ups in their technological approaches. He goes on stating that many technology-driven start-ups spend too many resources on refining their technology, but that most companies rarely become successful with their first or even second or third attempt at creating a viable business. Hence investors need to evaluate if and how easily the technology created in a tech venture can be repurposed for another product use cases and if (in the case of written code) it is bound to a specific hardware platform or to other software the technological setup is aiming at.

2.4.3 Growth Performance Indicators Golloto and Sungsoo (2004) mentions that the discounted cash flow method used by most investors during the dot-com time had a major weakness in the form of a strong reliance on the guesses and estimated values of the analysts being incorporated in the valuation process as fact. Even though such estimations included seemingly regular factors like potential growth rate, discount rates, price/earnings multiple and similar measurements, these growth rates were based on estimates of the number of visitors on the website pages in a specified period of time, average order size per visit and the frequency of orders from each visit, which in retrospect only provided limited insight into the actual financial potential of the companies (Gollotto and Sungsoo, 2004). Some companies at the time managed to convince the financial community to give them high valuations with even less viable indicators like free users on their page, the number of total internet users or the amount of bandwidth required to generate a sale (Corr, 2006). The frequent usage of ‘web traffic’ as an indicator for future demand in the methodology of evaluating internet companies during the dot-com bubble period, in the end, turned out to be wrong as website visitors did not turn to customers at the anticipated rate. Kettel (2002) provides an explanation of why such indicators were used anyway for valuations. He states that there was no suitable technique for making realistic projections of future cash flows, which is why many investments were done without a sound rational basis based on other unfamiliar indicators.

Because of the variety of and uncertainty about engagement indicators, as a simple check for general viability of the growth projections of a start-up Cusumano (2013) recommends to closely examine proposed milestones and their fit with the funds available to the company. He states that business plans outlining high financial investment requirements and a plan requiring years of operation before revenue is generated are an indicator for

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 13 September 2016

increased risk of failure as many things can go wrong in such a time-frame. Threats he mentions are established firms counterattacking, new competitors entering the scene, changing government regulations or technologies becoming outdated again.

2.4.4 Market & Competition The fact that the nature of competition in the digital economy is fundamentally different from the "old economy” competition, is summarized by Van Gorp and Honnefelder (2015). They state that

“The competitive process is characterised by competition for the market, rather than competition in the market. While the market is characterised by scale economies and network effects and has the tendency to tip, the market is also contestable. The contestability follows from entry strategies that are typically based on product and business case innovations, having the effect of radically changing the definitions of markets. This threat drives all digital companies, small and large, to prepare for the unexpected through constant innovation in all possible areas: new techniques, new products, new sales channels, new customers etc. including new combinations of the items mentioned before.” (Van Gorp and Honnefelder 2015, p. 161)

It seemed that investors and analysts did not understand the nature of this new competition, which is emphasized by Higson and Briginshaw (2000), who analyses how valuations if tech start-ups were performed and specifically how competition factored into the analytical models at the time. They point out that due to the novelty of the market, making statements about the (future) competition of a company operating in it are largely guesswork and that in making such assumptions, analysts examining dot-com companies often implicitly assumed the competitive environment to be favourable and with margins like in the old economy or better. Furthermore, financial analysts assumed that customers would be more loyal to an online business than the offline business they switched over from and with this completely disregarded the fact that switching from one online company (like a retail platform) to another would be much easier than the switch from offline to online. Even if competitors could attain great market margins, this would attract new competitors to the market, which would decrease margins of a company and force it to spend more resources on developing products and marketing instruments further. Higson and Briginshaw (2000) concludes that investors operating in the new economy based

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 14 September 2016

valuations on a superficial understanding of the mechanisms of competition in the new economy and recommend to develop much deeper knowledge of how those worked.

2.5 Other Factors that impacted Investment Decisions

2.5.1 Investors Expertise Singh et. al. (2015) point out that investors lacking tech knowledge (i.e. not having worked in a technology related field or received formal education in it) have a higher chance of mis-assessing tech ventures and are biased for or against IT venture investments depending on past successes or failures in the field. Interestingly, another of their findings is that in-depth domain knowledge of a certain technology led to mis-assessments too due to overconfidence and explain this with the tendency of experts to not scrutinize their own opinions. They hence recommend that investors should aim to build technology knowledge, but only moderately in order not to fall into the trap of expert overconfidence.

Chang et. al. (2016) confirms that an increasing number of investors lacking domain knowledge appears to be a common pattern in the build up of technology-driven market bubbles. They also mention that this pattern is so strong across past technology-driven market bubbles, that the number of investors investing into a rising sector they have no or little knowledge about could be used as an indicator of an inflating bubble. Furthermore, they point out that these investors in the past seemed to not have understood the cost structure and limitations of new technological developments, which was a major factor in their mis-assessment of the actual value of assets. One of the central conclusions of Gavious & Schwartz (2011) from their analysis of the importance of accounting principles in tech venture evaluation is that, while this expertise was often missing amongst investors in the dot-com bubble period, in the aftermath they did go through an adaptation process and improved their valuation models over time. According to Gavious & Schwartz this might also explain why there are so many conflicting theories on tech venture investor behaviour as those theories depend on what time period is examined.

2.5.2 Investment Paradigms & psychological Factors When examining stock pricing during the dot-com bubble, Ofek (2002) found that the one main pattern amongst all tech start-up investors at the time was an extremely optimistic view on and trust in IT companies’ potential even when confronted with strong adverse information. This optimism was so pervasive that even while a company changing its

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 15 September 2016

name did have no or little impact on its stock price in times before the dot-com bubble, companies changing their names to a term related to the internet or IT (such as appending “.com” to their the name) in the late 1990ies caused an average stock price increase of 53% in the days following the announcement of such a change, even if a company had only little activity with or in the IT sector (Cooper et. al., 2001).

This optimism also made investors less careful in their due diligence. Gavious and Schwartz (2010) finds that based on the premise that more dot-com firms would be able to repeat the success of companies like yahoo.com, evaluations during the dot-com times were carried out without gaining insights into the business model and service/product offers of the companies as long as they resembled companies that had proven to be a stock market success. This is further illustrated by Higson and Briginshaw’s (2000) finding that during the late 1990ies many businesses built tech features or products with the sole purpose to increase their valuation even though those features or products did not fit into their business strategy or add any substantial real value.

Already in 1998 Mills notes that effective valuation is difficult for technology start-ups as there is an unquantifiable lead time which can happen before the company starts generating profits for the investors. Investors prevalently used valuation indicators of revenue, profits and assets, which unfortunately had limited use for examining dot-com companies.

An additional interesting point on investment paradigms is made by Chang et. al (2016): Because of the mistakes made during the dot-com bubble in regards to the timing of investments and when it might be reasonable to divest, he suggests that investment objectives and a clearly outlined exit strategy for the investment should form the basis of evaluation for even minor investment opportunities. In the situations described above, again due to a lack of domain familiarity, few investors understood when to exit from their investments. Chang et. al. notes that with proper objectives in place, investors might have withdrawn from many of their investments earlier.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 16 September 2016

2.6 Developments since the Dot-com Crisis

Khanin et. al. (2008) analyses existing literature on criteria investment firms apply, getting back to the earliest dissertations in the field dating from the 1970s all the way to 2008. At that time, the dot-com crisis was a part of history and its effects processed. The outcome of the analysis sees several fields of criteria VCs count on post dot-com crisis:

● Top Management Team ● Market and growth ● Product quality in terms of its competitiveness ● Risk of the project ● Return on investment ● Exit options ● Quality of the deal ● Strategy of the new firm ● Customers and their potential behaviour ● Competition scenario

Morris and Alam (2012) examine the relation between traditional financial and accounting information and market valuation before, during and after the bubble. They confirm previous research results that the relation between traditional accounting information and market value was declining before the bubble, leading to the bubble itself. They find that after the bubble the trend reversed and traditional financial evaluation instruments became more important again. This is supported by Ning et. al. (2014), who finds that the Venture Capital industry became more careful in its investment strategies. They observe that investment rounds became smaller and are focussed on later stages in the development of young companies than they were before the dot-com crisis, likely due to more available data on the company and less risk in later stages.

2.7 Summary

The stock market bubble built up starting from 1997 and was characterized by a rapid increase in the market cap of internet-based companies with both institutional and private investors pouring money into the market due to the widespread belief that the internet would bring unprecedented growth and market opportunities. When investors realized

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 17 September 2016

around the year 2000 that many of the tech companies were unsustainable and would not be able to deliver the high future cash flows promised to the market, rapid decrease of market caps set in and the bubble burst.

As most tech companies raising money only had little historic data on their performance and almost none generated profit, the classical investment evaluation method - the discounted cash flow - was not usable to get to a meaningful valuation. Hence, investors relied on other indicators like the price to sales ratio, revenue growth numbers or - in later stages of the bubble - on product engagement and growth performance indicators like the number of website visitors or the number of total internet users, many of which turned out to be insufficient as a basis for investment decisions. Besides examining these quantitative factors, tech investors also tried to get a deeper understanding of a business by analysing qualitative factors like the business model or technological setup of the evaluated start-ups. These qualitative analyses, however, were often superficial and biased due to many investors’ lack of education and experience in the tech sector. Also contributing to over-valuations was a general optimism that led to lacklustre due diligence when analysing start-ups.

After the dot-com crash, investors returned to more conservative views on investments in the tech sector and developed evaluation models including a more diverse set criteria and more cautionary financial evaluation processes.

2.8 Open Questions

According to more recent research, there are still several open questions in regards to investor behaviour. Elnathan et. al. (2010) states that while there is some research on the valuation of publicly traded companies, there is only little research literature available on the valuation of private companies by financial experts. They explain this with the general difficulty of accessing documentation for valuations.

While Hsu et. al (2014) finds that there are significant differences in the decision criteria for investments between venture capital funds and angel investors, they recommend future research on how these two groups put weight differently on factors when making an investment decision.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 18 September 2016

Wang and Wang (2013), after analysing the connection of VC investments and overvaluation, pose the question if VCs actually drew lessons from the experiences during the dot-com bubble and altered their behaviours based on that.

Furthermore, Valliere and Peterson (2004) points out that there is only little research on how investors perceive data and take decisions during a bubble.

2.9 Positioning the Research Question

The research question, if the current tech market is steering towards a new bubble, became prominent again in the media (see Vardi 2016, Mims 2016, Cohan 2016, Mahmood 2015). Also, financial analysts and economists started warning of an impending bubble burst in recent years (see Nathan 2015). Nevertheless, only little scientific research can be found on comparing the current situation with the one during the dot-com bubble. Authors like Curwen (2015) briefly touch on the subject, but restrict it to short, purely macroeconomic market analysis.

This small study can give an indication of what factors investors are taking into consideration for their investment decisions in 2016 by comparing the answers in the interviews with what was pointed out to be prevalent investment behaviours during the dot-com bubble time. It will contribute to deepening the understanding of developments in the venture capital industry and provide an exploratory indication of how these developments might be a factor in the potential build up of a tech market bubble.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 19 September 2016

Chapter 3: Research Methodologies

3.1 Introduction

This chapter explains the methodologies and strategies that were used for the purpose of this research. It explains what different kinds of methods and strategies were examined, the reasoning behind the ones chosen and what limitations those have. After that, this chapter explains how the research was carried out and the type of data that was collected.

3.2 Research Objective

This research is an exploratory study with the aim to examine how the strategies of investors in tech start-ups have changed in 2016 in comparison to the strategies applied by investors during the dot-com bubble time in the late 1990ies. The primary research question to be answered is Parallels and differences to the dot-com bubble - are there indications of investor behaviour leading to another tech market crash?

The corresponding sub-questions are: 1) In which aspects do tech start-up investors follow lessons and recommendations from the research on the dot-com crisis in their investment strategies in 2016? 2) Which mistakes done during the dot-com bubble period are tech start-up investors repeating in 2016? 3) Is the tech investment market steering towards another financial crash?

3.3 Research Philosophy

According to Saunders et. al. (2009) the first choice a researcher should take, is one about the philosophy they want to follow in their research. He mentions that despite the fact that there are different formalized philosophical paradigms, a research question hardly ever fits into only a single one. The research topic of this dissertation indeed could be approached by various angles and there is a variety of research papers on the topic with a variety of philosophies underlying them as illustrated in the literature review. As a framework for deciding on the research approach, Saunders (2009) also points out four underlying philosophies: Positivism, Realism, Interpretivism & Pragmatism. These will be

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 20 September 2016

briefly elaborated on in the following paragraphs and a decision about a philosophy explained.

3.3.1 Positivism Positivism in research is associated with the mindset and approach of a natural scientist trying to achieve the most objective insights possible. This implies the attempt to create observable, measurable research and the test of clearly defined hypotheses. This approach is most commonly (but not exclusively) associated with quantitative research methods (Saunders, 2009). Orlikowski et. al. (1991) notes that this is the predominant research paradigm in IS research and that they believe this dominance of positivist research has provided only a limited view on information systems topics. They hence encourage the use of a greater diversity of philosophical and methodical approaches in the field.

As this thesis is touching an area where IS research and accounting research (the research of financial reporting) overlap, it is important to note the similarities in the approaches in the two fields. Chua (2011) emphasizes that there is a similar pre- eminence of positivist approaches in accounting research and similarly recommends the choice of research philosophies other than positivism for gaining new insights into the financial field. Because of the lack of other research approaches and the focus of the research question also on less quantifiable aspects of decision-making, positivism was not chosen as the underlying philosophy for this research.

3.3.2 Realism While realism also assumes the existence of an objective reality and tries to delve into a research topic using either qualitative or quantitative methods, according to Saunders (2009) it also assumes that the researcher has a tendency towards being biased based on their enculturation and socialization. This is, as Mingers et. al. (2013) states, because realism considers knowledge to be always influenced by its respective historical and individual context.

This philosophical approach was considered for this research, but rejected, as financial investment strategies are based on the (ultimately artificial) construct of the monetary system and stock market systems, which are largely based on mathematics paired with psychological factors.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 21 September 2016

3.3.3 Interpretivism Interpretivism puts great emphasis on the social actors in the chosen research field and hence requires an in-depth interpretation of the collected data based on and examined in parallel with the meanings we give to these roles. Walsham (2006) points out the distinction into “outside” and “involved” researcher in interpretive studies. An outside researcher is not directly involved in the field and can as such give a less biased view on the gathered data. An inside researcher, on the other hand, is involved personally with the research subject at hand and in the extreme form of the approach even consciously trying to influence the outcomes of the research subject. He notes that the inside researcher might get so acquainted with the perspectives of the people she encounters in the field that the advantage of having a fresh view on the situations studied might be lost. Because of this danger, an outside stance was chosen for this research.

Chen & Hirschheim (2004) analyses if IS research has diversified since Orlikowski et. al. (1991) points out the dominance of positivist research methods and came to the conclusion that despite the increase in research carried out under interpretivist paradigms, it is still far from being a widely used research philosophy in IS.

The subject of this dissertation is the decision models of individual actors and organizations, however, there are also quantitative aspects to be considered, so interpretivism was not chosen as the research philosophy.

3.3.4 Pragmatism Opposed to the rather strict classifications of the above philosophies, Saunders (2009) explains the approach of pragmatism as one that considers a variety of methods to be used and mixed with each other in order to gather the most suitable data, rather than sticking dogmatically to one way of approaching research.

As pointed out in previous chapters, there is a great variety of theories about the reasons for a tech market bubble forming many of which are rooted in a positivist and quantitative approach. There are, however, also some research papers related to the topic that are rooted in the interpretivist philosophy.

As this dissertation deals with the topic of how people perceive and understand the world and take decisions and these decisions are based largely on quantitative models and have quantifiable outcomes, it is required for the researcher to gain a direct insight into the

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 22 September 2016

thinking processes both from a quantitative and qualitative perspective in order to answer the research question. Because of this, pragmatism was chosen as a research philosophy and the answers to the interviews will be both analysed quantitatively and interpreted qualitatively.

3.4 Research Strategy

As the literature review chapter shows, most research in the field has been done based on secondary research with mainly quantitative approaches analysing financial decision models and financial market data. This research focuses on gathering primary data in order to expand the body of knowledge beyond ex-post market data analysis. For this purpose, a variety of primary research methods were considered that are briefly explained here.

3.4.1 Survey Pinsonneault and Kraemer (1993) describes surveys as a quantitative method for the purpose of gathering data on the population a researcher is studying. Data is gathered from a subset of this population and used to generate descriptive findings that are generalized to the entire studied population. Saunders (2009) points out that surveys need to be standardized and emphasizes the importance of having a representative sample. Surveys, as Pinsonneault and Kraemer (1993) mentions, are not suitable for studying a phenomenon that needs comprehension of the context of the time the phenomenon occurs or occurred. Bell (1996) also states that surveys are prone to biases as it is possible to not get enough responses or responses of bad quality.

As the individuals in the financial investment industry are considered protective of their strategies due to the large sums of money at stake and surveys are less likely to yield a response due to a lack of legitimation through personal endorsement (Sieber 1973), this method was not chosen for this research.

3.4.2 Case Study Pinsonneault and Kraemer (1993) also comments on the value of case studies and describe them as useful when trying to understand how a phenomenon will develop in an authentic setting. They also mention that case studies are best used when a researcher is aspiring to understand the impact a context might have on the subject of her study. Yin

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 23 September 2016

(2003) describes a case study as a research strategy that focuses on a specific phenomenon that is examined in a real life context with the use of a variety of observation and analysis sources. He emphasizes the importance a context has for the respective phenomena and that such a context distorts the results of the controlled “laboratory” conditions of many other methods. Yin also highlights that case studies pose the danger of creating a bias in the investigation, hence this method was not chosen for this research to enable a broader, exploratory view on the topic and prevent a bias in interpretation.

3.4.3 Interview Saunders (2009) explains 3 types of interviews generally used in research: ● Structured - with a set of standardised questions ● Unstructured - an informal conversation in which participants speak completely freely ● Semi-Structured - with some standardised questions and overarching themes that guide the conversation and for which questions can be taken out or added if it fits the context For the purpose of this research, semi-structured interviews were chosen as this approach helps probing answers further and to identify deeper patterns and meanings of what is initially said. Additionally, to the variety of investment approaches in the tech investment industry, data is expected to provide broad results and the interview method will allow a deeper probe into potential root causes of opinions and behaviours, which would be difficult using surveys or other quantitative analysis methods.

Silverman (2007) points out that the way the interviewer interacts with the interviewed person impacts the collected data. Therefore, special precaution was taken during the interview to avoid influencing interviewees too much. This was also considered in analysing the interviews.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 24 September 2016

3.6 Research Process

3.6.1 Research Targets

The target group for the study were individuals working in private or public institutional Venture Capital Funds where they are decisively involved in investment decisions in early stage tech companies or so-called “angel investors” in the technology field. The aim was to interview at least 10 individuals.

TABLE 3.1 - Comparison of institutional investors and angel investors (based on Ibrahim and Rogers, 2008)

Institutional Venture Angel Investors Capital Investors

Affiliation Private investment banks or Individual (no affiliation) funds

Governmental investment funds

Type of funds invested Funds of other people Own funds of angel investor

Stage of investments Expanding companies, Early stage, usually the first companies preparing for IPO year of a start-up’s existence or private sale

Typical Background Financial analyst Ex-Entrepreneur

Involvement in startup Board seat Hands-on support

Investment sums Typically between $2 million Typically between $100.000 and $10 million, but can and $1 million, up to $2 surpass this million

3.6.2 Research Design

For the purpose of this research, a questionnaire was created that was used as a baseline for the interviews. As the semi-structured interview approach was chosen, not all questions were asked in the same form to all participants. However, all interviewees were always asked to answer the most important exploratory questions.

The questionnaire is based on the findings of the literature review and aimed at checking if the various factors pointed out in previous research as relevant to either the creation of

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 25 September 2016

the dot-com crisis or relevant factors for successful tech start-up investments are considered in current investment strategies. Additionally to open questions about why and how certain factors are considered in investment strategies, a number of closed questions are asked in which participants are requested to answer on a rating scale, for example about the importance of a certain factor or topic. This is done in order to additionally gain data that would allow more direct comparison of the answers of the interview participants.

The questions are grouped to cover specific themes identified in the literature review as relevant for investment decisions: ● Financial factors considered ● Non-Financial factors considered ● Factors that are not part of a decision-making model, but influence decisions indirectly Additionally, demographic questions are asked for added context.

As most of the interview participants were located in different countries than the researcher, only 2 interviews were carried out in person. The remaining interviews were carried out via Skype or phone.

3.6.3 Data Collection and Analysis

Walsham (2006) points out the difficulty qualitative research has in getting access to suitable organizations and persons. As large sums of money are involved in the financial industry and investment strategies are an important factor in staying competitive, for this research there were some initial difficulties in acquiring suitable interview partners. After obtaining personal endorsements from venture capital investors in order to establish trust with potential interview participants, interview participants were acquired quickly to carry out the research. Given the global nature of the topic, attention was given to have diversity in countries and specific roles the interviewed investors represent. Overall 12 interviews were carried out.

Given the number of questions and depth of answers on more complex topics, the originally estimated interview length of 30-45 minutes was not sufficient and interviews almost always took nearly an hour or, where this was not possible, could only cover a smaller selection of questions. The interviewed investors were generally very helpful,

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 26 September 2016

open and friendly, but most of them - especially representatives of larger institutional funds - emphasized the importance of keeping their identity anonymous. Hence, no personally identifiable information are included in this dissertation and quotes are given anonymously.

TABLE 3.2 - Overview of Study Participants

Type of investor Regional Focus Date of interview

Institutional Eastern Europe 02.08.2016

Angel USA 03.08.2016

Angel Western Europe 03.08.2016

Institutional Western Europe 05.08.2016

Institutional USA & Europe 06.08.2016

Institutional Western Europe 07.08.2016

Institutional Western Europe 08.08.2016

Institutional Europe 12.08.2016

Institutional Global 15.08.2016

Angel Western Europe 16.08.2016

Institutional Global 17.08.2016

Angel South-East Asia 17.08.2016

The interview analysis is carried out following the data organization process and principles described in Crabtree and Miller (1999, p127 - 138). Both the template approach of using pre-defined categories that have relevance to the research hypothesis and the editing method of reducing texts to summaries to show a clearer interpretive truth were used in the qualitative analysis. The quasi-statistical method of turning textual data into quantitative data that is also described by Crabtree and Miller (1999) is also used to add context the findings produced with the aforementioned analysis methods.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 27 September 2016

Chapter 4: Findings

4.1 How the Research was conducted and analysed

The Interviews were conducted via Skype or phone and recorded using recording software after getting consent by the participants for the recording. Additionally, the interviewer took notes during the interview. The recordings were transcribed by the researcher directly after the interview and reviewed together with the respective notes afterwards. Results were noted down in a general interview analysis chart and mind map. Since the questions were already grouped according to general themes pointed out in the research (knowledge of investors, financial factors for an investment decision, non- financial factors for an investment decision & learning processes), this provided the initial grouping of answers.

After all interviews were concluded, the answers were reviewed again and the larger themes modified accordingly to accommodate for the newly gained insights. As a result of that, the additional theme of “Current hype and potential bubble” was added as it was a prevalent theme in the interviews and the point “Founder Team” expanded as a separate section as it was emphasized so strongly across all interviews. As patterns amongst the interview answers became more evident at this stage, they were reduced to thematically relevant statements and regrouped according to sub-themes related to the larger pre- defined themes. Additionally, answers were quantitatively analysed in regards to the inclusion of factors into investment decisions and mentions of new factors and arguments and this analysis put into tables that are added in this chapter to provide a context where necessary. The interviews were then reviewed a third time and quotes directly related to the various themes extracted and attached to them to allow for tracing back argumentations and findings to the actual interviews.

The process of developing the themes was iterative and required several regroupings of the findings. As in the course of the analysis it became evident that angel investors and institutional investors consistently differed in certain key areas and opinions, the findings are split up according to that categorization where appropriate.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 28 September 2016

4.2 Overview on Participants

Investor Type & Investment Volumes As outlined in chapter 3, the targets of the research were individuals playing an important role in tech start-up investment decisions. 8 out of the 12 interviewees are institutional investors and 4 angel investors with individual investment volumes ranging from 3500 USD on the lower bound to 56 mio USD on the upper bound. Typically angel investors in the sample have on average both lower investment volumes and fewer investments than institutional investors.

FIGURE 4.1: Organisational Setup & Investment Volumes

Geographic Focus The geographic focus of investments is typically regionally oriented. The main reason stated for this was that the networks of investors both for experts needed for an evaluation and in the start-up scene are stronger in their respective countries and regions. This plays an especially strong role for Angel investors who lack the infrastructure of established institutional investors. Additionally, the two interviewed investment managers of state- funded venture capital funds have the explicit mandate to fund only companies with operations tied to their respective countries.

4.3 Findings on relevant Investor Knowledge

One of the recommendations in the research on the dot-com crisis is that tech investors should generally have in-depth knowledge and experience in the tech industry to enable them to carry out more sound due diligence. Interview participants were hence asked to evaluate the importance of relevant expertise and how they would assess their own level

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 29 September 2016

of knowledge. Additionally, they were asked on their educational and professional background to assess how much this might aid them in evaluating tech start-ups.

4.3.1 Educational and Professional Background of Investors According to the answers, most institutional investors still have both in education and profession1 a background in business and more specifically finance. The interviewed angel investors - a role which in this form largely didn’t exist during the dot-com bubble period - on the other hand do have experience in the tech industry. All of the participating angel investors with tech industry experience mentioned that they only invest in companies specifically in industries they themselves have professional experience in.

FIGURE 4.2 - Educational & Professional Backgrounds of Interviewed Investors

4.3.2 Market Knowledge There was a general sense amongst the interviewed investors that market knowledge does play an important role in the investment decision. Two main reasons were stated for this: Firstly, without sufficient knowledge of the market, it would be difficult to verify and challenge assumptions of the financial model and business model a start-up provides. Secondly, once an investment happened, a lack of market knowledge renders an investor unable to give much support in building the company. One of the participants also mentioned that building market knowledge usually leads to establishing market networks and that an improvement in the former would hence make it easier to acquire and bring

1 “Professional Background” refers to the industry the participant worked in the longest before working in the area of venture capital.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 30 September 2016

experts into the company. The interviewees, who gave the importance of market knowledge lower ratings, explained that in order to grasp a company well enough, one only needed a general understanding of the market which can be gained rather quickly and that the founders should be the ones to have, digest and present an in-depth market knowledge.

The interviewees consistently evaluated their own market knowledge as good, but not the best possible. When asked to justify this rating, the angel investors brought forward their insight into the industry of their investments. Institutional investors mainly mentioned that they use extensive benchmarking with other companies in the market for understanding the dynamics and that they could always bring in experts to compensate for their own lack of knowledge:

“As long as you have trusted advisors who can take a look at things, that is kind of how we operate, when we don't know a lot or we feel we have gaps in our understanding, we bring in people who can kind of help us figure it all out."

It was also mentioned that being in the business of start-up investment requires constant learning:

"Lots of markets are in transformation - if you think you knew a market in the past, it is not necessarily the same in the future"

4.3.3 Knowledge of Technology Trends While market knowledge helps understand the specific problem a start-up is trying to address, the interviews showed that the knowledge of larger technological trends might help similarly or even more to make sound investment decisions as participants rated this on average similarly important as market knowledge. Reasons given for high ratings were that it would be hard to evaluate technical capabilities of the team if one does not understand the technology and that knowledge of the tech trends is important to stay ahead of the curve, invest in the future and identify which companies are going to lead those trends. One of the investors pointed out that

"If you are not looking in the future, if you don't read analyst reports, if you are not looking what are the next big things, you failed as an investor. Because you invest for the future."

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 31 September 2016

Two of the participants that rated the importance of tech knowledge with 3 or lower mentioned that it’s only important that the founder team is great in regards to the technology rather than the investor, as the founders need to be the ones able to adapt to new trends and developments quickly. Hence they argued that an investor only needs to evaluate the team in this aspect. When asked how they then can understand the skills of the team without sufficient insight into the technology, they mentioned that for this evaluation, too, they could hire external experts.

For rating their own knowledge in technology trends, interestingly some of the lowest ratings came from institutional investors investing the highest sums within the group of interviewees. This could stem from a more cautionary approach of institutional investors and the realization that trends are hard to predict as illustrated by this quote by one of the participants:

"In some of these very emerging areas, there is still nobody going to say 'yes, absolutely, this is going to work', because, inherently, nobody knows if it is really going to work.”

Two of the European interview partners pointed out a disadvantage in the technology background of investors in Europe. As one of them stated:

“Nobody is a technical person here. That is a general thing in Europe, actually. In the US most VCs were engineers who turned into entrepreneurs who turned into VCs. In Europe, most of the VCs are former investment bankers, real estate fund managers, etc. And that makes a huge difference in how you look at everything.”

A hint that this might be true is that the only interviewed VC representative with experience in the tech industry was based in the US.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 32 September 2016

TABLE 4.1 - Ratings of the Importance of Knowledge and Knowledge Self-evaluation

Market Technology Trend Own Market Own Technology Knowledge Knowledge Knowledge Trend Knowledge Importance Importance

Angel 3.50 3.75 4.13 3.625 Investors

Institutional 4.38 3.71 4.06 3.5 Investors

Total Average 4.08 3.73 4.08 3.55

4.3.4 General Findings on Investor Knowledge Some participants mentioned that founding start-ups and investing in start-ups has become more trendy in recent years and that this might have a negative impact on the general investment situation due to an increasing number of both entrepreneurs and investors with a lack of experience and knowledge in the tech scene:

“You see new funds all the time, or angel investors who don't really know anything about how the investment process works or people who are just kind of dabbling in investing. I think that is where it is - the everyday average Joe investing in internet stocks - that is where you get a lot of not-smart money in the market. There is where I think this can go bad."

However, it was also pointed out by the majority of interview partners that most young tech companies are - differently from the dot-com bubble period - not traded on the public markets. It was further pointed out that public markets - which contain many small private investors - act much more impulsive, emotional and irrational and have little to none knowledge about markets or technology trends. Without those retail investors in the picture, the interviewees further elaborated, the allocation of investments follows more sound investment principles and due diligence. Additionally, effects of negative developments are also contained within the professional investment space this way.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 33 September 2016

4.4 Findings on financial Factors taken into Consideration

4.4.1 General Approach The interview partners confirmed that even though the discounted cash flow (DCF) method is the main method in investment evaluations in the financial industry, it is - as also pointed out in the research - insufficient for start-up investments. While investors during the dot-com bubble times struggled to come to terms with this (as was pointed out in the research), one could argue that investors in 2016 seemed to have accepted and embraced this wholeheartedly. Only the more advanced institutional investors mentioned that they use DCF proxy calculations and simulations and even in those cases, this is only used as supplementary information, not as the main evaluation method. One of these investors even called DCF calculations a mere “sanity check” for their investments.

The majority of interview partners also pointed out that the emphasis in the evaluation is more on financial KPIs relevant for follow-on investments and exits to check the attractiveness for potential acquirers or investors down the line.

4.4.2 Financial Key Performance Indicators There was a general sense amongst interview participants that any financial KPIs were of very limited use in the early stages they invested in tech companies. It was pointed out that financial KPIs at that point are at best able to indicate consistency and “sanity” of the business model and trends in the development of the company. All investors agreed that this only provided meaningful insights by looking at the KPIs in the context of the entire model and that relying on individual KPIs for investment decisions was not helpful or even misleading. Participants also pointed out that the earlier in the life of a tech start-up a valuation was done, the less helpful financial KPIs were. Nevertheless, as table 4.2 shows, the majority of investors did have a general evaluation approach or model they tried to apply and most of the pointed out factors were considered by more than half of the interviewed investors for their decisions.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 34 September 2016

TABLE 4.2 - Considered Financial Factors

Considered Yes No No answer

General Model 8 4 0

LTV 11 1 0

Market Opportunity 7 3 2

Revenue 11 1 0 Financial factors Profit 1 9 2

Competition 5 5 2

Risk 3 7 2

Exit Potential 8 2 2

TABLE 4.3 - Newly Mentioned Financial Factors Newly mentioned factors Mentions

Cost / CAC 6

Liquidation preferences 2 Financial Factors Cash burn rate 3

4.4.3 Customer Lifetime-Value One set of questions in the interview was concerned with Customer Lifetime-Value (LTV), as this was recommended in the research as an indicator for evaluating the sustainability of a company. LTV received the highest average importance ranking as a helpful financial measurement across all interviews. However, while it was pointed out that this is the best financial measurement to evaluate if a company can survive and be successful, most interview partners only mentioned they are looking at “repeat use rate” or “stickiness” as illustrated by this statement:

"I want to see customers who are completely locked in with the product"

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 35 September 2016

Participants rating the importance lowly pointed out that in early stages, an analysis should be rather about customer engagement measurement. They stated that those could only be seen as proxies for future LTV development, but that unfortunately engagement and LTV growth are not necessarily correlated with each other in every venture.

4.4.4 Market Opportunity While the actual financial performance an early stage start-up will have can only be guessed with little certainty, according to the interviewees evaluating a market can be done with more clarity. Hence the majority of investors in the study puts emphasis on this in the evaluation process. However, it was also pointed out that with a flexible product or technology different markets could theoretically be served in many cases, which can reduce the risk of total failure for a start-up. It could be argued though that this thinking about market flexibility can introduce arbitrariness into market estimates.

Participants rating the market opportunity as a less important factor pointed out that markets can change quickly and that the market size (which is usually how the market opportunity is quantified) is less important than the ability of a start-up to capture a portion of the market. These participants also pointed out that they prefer smaller niche markets with the potential to be dominated entirely by the start-up:

"It doesn't have to be ‘the bigger the better’ - it could be a niche market but with potential for growth and domination on that niche market which makes the company very attractive for being sold to a big player later."

On the other hand, some of the participants who gave the market opportunity a high rating said that they do not want to go for small markets at all. As they are looking for massive exit opportunities, these small markets would not be able to provide those. One participant expressed this way of thinking with:

“We are not fishermen, we are whale-hunters.”

This seems to be coming from a fundamental difference in investment philosophy as both “niche market” and “big market” investor groups consisted of both angels and institutional investors and both investors with investment volumes from the lower to the upper bound across the sample.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 36 September 2016

For assessing the market opportunity, most investors rely on analyst reports and external experts they bring in specifically to assess the market opportunity for a start-up. The ones that try to access not yet developed or existing markets (two out of the 12) also try to evaluate a start-ups offering in the context of macroeconomic trends and similar markets as proxies. Most of the participants also monitoring the moves of large cutting-edge tech companies like Google or Facebook who act both as early market seismographs and market creators.

4.4.5 Lack of historic data on financial KPIs The research had shown that the lack of data on the performance of young start-ups (as this performance does not exist yet at the time of a seed investment) was a major problem for evaluating dot-com start-ups and that this led to disregarding model evaluation all together in many cases. Most interviewed participants explicitly mentioned that they tried to avoid such behaviour and stated that they demand at least some validation (see table 4.6) of a start-up product or service in the form of first customers. They would not invest at all without proof of such first customers. An in-depth look at data on few first customers is seen as the best way to make up for the lack of larger dataset as it would be the case with established companies:

"Even when they are small and have only a dozen customers, and once you study who are these dozen customers, how they are representing the geography, the total field, you can kind of scale it to the whole market"

Additionally revenue - and especially revenue growth - was seen as a helpful indicator for a validated business model and the potential of a start-up to scale up operations, though some cautioned to always at the same time keeping the cost and cash burn rate in sight (see table 4.3).

The few investors in the interviews that were open for start-ups that are not able to provide any historic data on their company at all emphasized that in such cases they only invest if there is an exceptionally great team with a sound market opportunity:

"If they just say 'well, we have absolutely no data because I haven’t really done anything yet' but we believe what the technology sounds, their approaches sound, if they got a plan, people seem like they can execute, the market potential is huge

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 37 September 2016

if it works, ok, let's. You just got to be comfortable with having no data and just be able to recognize talent, and the fundamental soundness of their approach."

This same investor however also complained that the adamant demand for initial customer data led to a “death valley” in the first months of existence of start-ups. This death valley did not exist before and during the dot-com bubble times and has the effect that a lot of great ideas - especially from younger founders - are never being realized.

4.4.6 The Problem of estimating future Profits All interviewed participants acknowledged that it was not helpful for investment decisions to evaluate potential profits and that forcing a start-up to provide a profit analysis had only very limited value due to the lack of historic data:

"[It is] hard to estimate future revenues, it is always shooting in the dark."

Only one investor mentioned they work with this, but when doing so adjust all assumptions down:

"As a rule of thumb, you can say that everything takes longer and is more expensive than planned"

4.4.7 Competition Participants said that competition evaluation is usually done as part of the market opportunity assessment and a major part of the analysis is outsourced to the start-up itself which is then scrutinized and cross-checked.

Interviewees were however divided on the importance of competition for an investment decision. Several mentioned that they see competition rather as validation that there actually is a valuable market opportunity. Vice versa - they think that if there is a market, more competition will show up sooner or later anyway. This is contrary to the assumptions of investors during dot-com bubble time period, who assumed that competition would stay static or get more favourable over time (Higson and Briginshaw, 2000).

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 38 September 2016

TABLE 4.4 - Importance of Commonly Pointed out Factors Impacting the Financial Model

Customer Lifetime- Market Opportunity Competition Value

Angel Investors 4.16 3.5 4.33

Institutional 4.58 4.43 4.07 Investors

Total 4.44 4.15 4.15

4.4.8 Risk & Exit Scenarios When asked about how they would evaluate and mitigate the risk of the start-up failing, most participants note that they don’t have any specific process for risk evaluation. The main actions in that direction are to minimize the risk of failure with extensive due diligence and protecting themselves with investment protection clauses in the investment contract. The latter was especially important for the interviewed institutional investors that wanted to offset the high risks with high liquidation preferences (see table 4.3). They explained that such liquidation preferences only protect against “bad” cases (selling for a lower value than they had hoped for), but not the worst case of bankruptcy. Generally, amongst participants, the possibility of start-ups failures was seen as unavoidable. None of them talked about how to avoid a total failure of a start-up, but only about making sure a start-up would have the potential for a large exit:

"I don't think we are afraid of the risk of failure, specifically, we are afraid of just not growing big enough rather"

"Europe, specifically, where you don't have a lot of role modelling companies growing really big, there is a higher risk of companies staying mediocre. And it is not like a failure, but for us, it is somehow."

As such, evaluating potential exit scenarios - by benchmarking the company against other companies in the space that had a successful exit and factoring in assumptions for this specific company or by analysing potential future acquirers - was part of the process for some of the interview participants. Others - especially angel investors - however said that

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 39 September 2016

thinking about an exit from the start was the wrong approach towards an investment and just promoted unsustainable thinking.

4.5 Findings on non-financial Factors taken into Consideration

4.5.1 Product related Key Performance Indicators When asked about product KPIs, the investors mentioned mainly factors related to repeat usage (daily active use, repeat usage) or “stickiness” (as it was called by the investors) of the product/service (see table 4.6) and conversion towards monetization. Since they invest so early in the development of a company, many of them also analyse the qualitative feedback from customers and examine trends in the aforementioned product data. The KPIs that were mentioned are very similar to the ones brought up during the dot-com bubble times. However, as one investor commented, since the dot-com time-period the measurement & analysis of such factors has improved significantly and is much more closely tied to evaluating the business model and generating revenues and eventually profit.

TABLE 4.5 - Considered Non-Financial Factors

Considered Factors Yes No No answer

R&D 1 5 6 Non-Financial Engagement KPIs generally 12 0 0 factors Business Model 12 0 0

TABLE 4.6 - Newly Mentioned Considered Non-Financial Factors Newly mentioned Factors Mentions

Initial validation 5 Non-Financial User Feedback 3 Factors Stickiness 6

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 40 September 2016

4.5.2 Business Model While they alluded to it throughout the interview, the interview participants only occasionally used the word “business model”. When asked directly about it, everyone agreed that understanding the business model was important. As one investor stated:

“The earlier an investment, the more important to understand this as an investor yourself and having a hypothesis on how this will play out on the market”

However, the understanding of how a business model should be evaluated differed substantially between investors. All of the interviewed angel investors referred to the importance of the question “would I use it myself?”, while none of the institutional investors referred to such a rather subjective factor. Instead, they emphasized the importance of evaluating not only the use case for customers, but if a business model actually made economic sense:

“Some businesses are great businesses, but they are not good investments. Because they are too expensive to build or can't grow big enough or have other dynamics that prevent them from exiting at some point.”

4.6 Findings on the Founder Team

4.6.1 Importance of a good Founder Team for a Tech-Company The importance of the founder team was on average rated the highest by all interviewees and many of them emphasized that from all the discussed factors throughout the interview this one was the most essential for their investment decisions. The strong emphasis on the founder team was explained by the fact that the founders were the central actors in the development of the start-up. They needed to be the ones to figure out how to adapt a product to changing market conditions, failing product hypotheses or other situations that required a strategy change. Also, it was mentioned that the founders were vital for attracting and motivating talent to work in the company. One of the investors summarized this view in the following way:

"If the management is strong, even if they don't have the right product yet, they are manoeuvrable enough, devoted enough and hard working enough, they can find

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 41 September 2016

the way why the product is not right yet, and they can update to the needs of the customer. They can change, they can adapt and can just really go and get it.”

Another interviewee also pointed out that a great team is even more important the younger the company is, as in that stage there are even more unknowns that require creativity, talent and perseverance to be tackled.

However, one of the participants rated the importance of the team rather lowly and explained that

“It’s important, but we might add value here ourselves. If there is quality missing in the team, we might be able to add this from our own resources. We can add a person into the team ourselves with the quality they are lacking.”

4.6.2 Sought out Qualities of a Founder Team of a Tech Start-up There were a number of qualities mentioned that investors look for in a founder team. Besides tech skills (see table 4.7) the most often mentioned factors were positive team dynamics, industry experience and the ability to adapt, be flexible and coachable (see table 4.8). The very high requirements of investors in regards to founders skills and devotion to the company was emphasized strongly by investors:

"We are looking for people that don’t have a plan B, this is what they are doing, and if we don't get involved they are going to figure out another way to raise the money to move it forward"

"If somebody gives you money, and you are like "yeah, we just need money, we can give you updates but we don't want to work with you", that is for me a red flag as well"

Interestingly though one of the institutional investors also mentioned that

"they shouldn't be too experienced, so they should be hungry, in a way, and flexible".

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 42 September 2016

It could be argued that this investor sees a trade-off between experience and the required dynamic mindset.

TABLE 4.7 - Considered Factors in regards to the Founder Team

Av. importance rating Yes No No answer Considered Factors (1-5)

Generally 12 0 0 4.88 Founder Tech Skills 8 2 2 4.125 Team Networks 1 8 3 2.75

Curiously, tech skills were not mentioned at all by any of the participants, so a follow-up question was asked about this. In regards to the importance of such skills, the interviewees were split: some expressed the opinion that good tech people could just be bought in or attracted due to the leadership of the existing team. The majority, however, said that the person responsible for technology in the founder team should be very strong. An investigation into this phenomenon would be valuable for further research.

TABLE 4.8 - Newly Mentioned Considered Factors in regards to the Founder Team

Newly mentioned factors Mentions

Team dynamics 7 Founder Industry Experience 8 Team Flexibility / Coachability 5

4.6.3 Networks The existing networks of a founder team were generally rated as not important and it was pointed out that investors can just bring in their own networks, which they saw as one of the main benefits of having an investor in a company. Two people rated networks highly: one person (working in the B2B field) mentioned that in B2B networks are significantly more important as this is how founders can get access to potential customers, another said that this helped understand the market better. The latter however recommended caution in regards to good networkers:

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 43 September 2016

"It would be negative, if they are only networking and going to every event, giving speeches and bragging about their business, although they should be focussing on the product and revenues and KPIs"

4.6.4 Evaluating the Founder Team Evaluating the founder team was called by a participant one of the most difficult and important responsibilities for an investor as there is no way to properly measure or quantify it:

“Team is the most important, but also most difficult factors to size up for an investment",

"Team is hard to nail down with quantitative measuring, so that's where this [intuition] comes in the most."

As so many “soft factors” need to be evaluated, most investors in this study put a strong emphasis on both a thorough background check and a close personal examination of the founder team in personal conversations, question and answer sessions and observations of the founders in various contexts. However, the approaches vary as illustrated by these quotes from three different investors:

“There are objective factors, of course, like the résumé, the recommendations [...]. But the rest is really subjective, we usually go to the office, we spend some time there with other people, not just the founders, we see how is the mood, even how does the kitchen looks like, very simple things. Are they consistent with what they say about themselves? Also, we look at the dynamics between the founders themselves, how they approach each other, do they have equal number of shares.”

“I usually sit with all of them together and then I want to talk to the senior team and send the founders out, and then I let the senior team talk about the founders. When they talk something different, there is something wrong. If they are telling the same, it is a good team.”

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 44 September 2016

“Also the history of the company, the way the investment material is prepared are all symptoms of how the team is set up.”

Getting a personal impression is so important for investors that they invest significant time into it themselves:

"I don't mind doing big trips to meet people in person and get a feel from them."

Approaches & principled pointed out were: ● Asking senior team about the founders and about company ● Looking at CV - experiences, industry, successes ● External recommendations ● Psychological evaluation (only one answered this) ● Feeling of trust ● Understanding that the founders do not have a “plan B” ● Personal liking ● Feeling you want to be part of that team

As this there is so much emphasis put on the founder team and evaluations are done with a strongly subjective note, one investor is calling for caution in this regard and points out that investors can also be blinded by excellent presentation and marketing skills of founders:

“There are people who are really good at raising, but those people are not necessarily the ones who can build great companies. Those are the ones that I think that can get VCs in a frenzy, and get this crazy competition going where the valuations are going up and up.”

4.6.5 Founder Team Missing in the Research on the Dot-com Bubble None of the scientific papers focussing on the dot-com bubble that were reviewed for the purpose of this dissertation mentioned the importance of founders. This is in stark contrast to the findings of several papers on venture capital investment strategies in general (Hudson and Evans, 2005 , McMillan et. al., 1985), according to which the founding team quality is an important criterion for investors. One could argue that the strong emphasis on quantitative analysis in both the field of IS research and financial research has led many researchers to miss this point in their analysis.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 45 September 2016

4.7 Findings on Decision-making & Learning Processes of Investors

4.7.1 The Role of Intuition in Investment Decisions While decisions in an investment context are put on a sound analytical basis, throughout the interviews many of the participants mentioned that in venture capital investment there are certain points, where intuition does play a role inevitably:

"Given the uncertainty, there is always a large portion of gut feel and belief in there. That's something that gets better over time, with pattern recognition."

When asked further at which points and in what ways intuition plays a role in their own investment decisions, one participant pointed out that it does help enhance less clear decisions:

"It's not just about falling in love with the venture. You look at the fundamentals of course. Intuition enhances your decisions then and turns a less clear "no" or "yes" into a clear one."

This was confirmed by others and it was stated in several of the interviews that ultimately an investment decision is an “educated guess” that should however always be based on the best data basis one can get.

Every single investor mentioned that intuition played the largest role when evaluating founders and several made it quite clear that this was a make or break factor:

"There is definitely an intuition element, especially when it comes to how well you connect with the founders"

"Team is hard to nail down with quantitative measuring, so that's where this come in the most"

It was also mentioned several times that intuition also played a role when it came to the market, technology trends and if the business model could work:

"Gut feeling comes all the time, especially on evaluating people, the market, future exits, it's very intuition-based, do you feel like these people can deliver or not, that this product will have a good market, positioned or not, do you think other players will jump into the market and then it will be too crowded or not."

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 46 September 2016

4.7.2 Avoiding Errors during the Valuation Process As the research pointed out that the dot-com bubble was also caused by not making sure the fundamental assumptions of an investment decision are sound, the participants were asked how they made sure that a valuation would be sound. Angel investors and representatives of smaller venture capital funds stated that they mainly relied on questioning the founder team about the underlying assumptions of the models they present and on doing a general consistency check when re-tracing all the parameters of the business model:

"If we can understand all the assumptions or the hypothesis is a closed one, I sleep well."

The representatives of larger institutional funds all mentioned that they would try to prevent erroneous decisions by (again) bringing in external experts and having a check of all investment decisions by investment committees with several experienced investors looking over them.

One of the smaller institutional investors also mentioned that additionally to all the above, they would also do a quick check on simple, human components:

"In the process, we quickly find things which are not so number-driven also. If you think you have a market in the US, and you are not willing to move there, it is impossible to have US customers from Europe, you have to be there. Those are discussions where we help founders to get their expectations right. It’s not just about the market."

4.7.3 Valuations Turning out to be Wrong When asked about how they deal with a situation where their valuation turned out to be wrong afterwards and the company ran into problems, the general opinion was that the founder team had to deal with it. However the interview participants were split about their own role, some said they should get involved in such a case, others that they should not meddle with the affairs of the founder team.

None of the interviewees mentioned anything about adjusting their own models, examining their way of thinking or similar learning processes.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 47 September 2016

4.7.4 General Learning Processes As none of the investors mentioned how they learn from their investment experiences, this was explicitly asked about towards the end of the interview.

TABLE 4.9 - Methods for Improving Decision Making & Learning

Method Mentions

Involving Experts 6 Decision “More eyes” principle 5 making &

Learning of Experiences 4 investors Knowledge Mgt (databases) 3

A number of participants simply stated that they believed that gathering experience in investment over time would enhance their general decision-making capabilities. Some mentioned that they tried to improve their investment capabilities by reading relevant literature or exchanging knowledge with other investors or experts and by always having more people looking over investment decisions (“more eyes principle”). Only three mentioned a more structured knowledge management approach towards this. Two participants mentioned that they keep track of investment valuations and trends in databases that they regularly checked and maintained and one mentioned that the VC she was working in has a “yearly strategic evaluation and planning session”.

4.8 Findings on Market Bubble & Market Hype

4.8.1 Opinions about other Investors Interestingly, opinions about other investors, in general, were not very positive. Most of the participants mentioned that they had the feeling no proper due diligence was performed by many investors and that a majority just followed trends and other investors’ valuations without performing their own due diligence process.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 48 September 2016

TABLE 4.10 - Answers on the Question "Do most investors fail in valuations?"

Question Yes / Probably No No clear answer

Do most tech investors 7 4 1 fail in valuations?

This opinion was expressed several times, but for a variety of reasons:

"Most of the investors don't have a really close look at the profitability, unfortunately"

“With VCs, I do think that a lot of it comes down to 'oh, it is Virtual Reality or it is Augmented Reality, let's take a look at this, we got to have some bets in this area', I think people can get a little bit lazy there."

"In Europe, what happens, is that there is a lot of EU and state money, it is ‘cheap’ money. And therefore, they have no limitation to spend it, they just spend it. They don't necessarily take a close enough look, and they are not necessarily good owners or managers of that money."

“That's a lot of emotional money going on, people want to be investors in a start-up and support the team, and the technology, and it's not only about the yields, so it's not only financial return, but it's social gratification."

On the other hand, it was also mentioned that different from the above-mentioned institutional investors, angel investors

“are almost too cautious, they do try to make it all about data, and they are almost too conservative."

One of the root causes of investment failures identified was that investors did not evaluate start-ups according to their ability to generate profit:

“most don't even care about generating profit, just about exiting”.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 49 September 2016

4.8.2 Hype & Bubble Opinions on potentially being in a bubble at the time of the interview were split. The participants had a differentiated view, but most thought that valuations on the tech start-up market were generally too high:

“Do I believe there is a lot of money being invested at valuations that are not justified and numbers and actual facts - but just by people just hoping to chase a good exit - yes I do. Does this mean there is a bubble and this bubble is about to burst - that is hard to predict.”

"I would say we are absolutely in a hype again, even if McKinsey and others are trying to tell you differently. That is reflected in the quality of deals in the market and valuations they get. That is reflected with very visible consumer media picking the topic up. And that is reflected by very light business models getting funded where you say 'I don't get it really'."

TABLE 4.11 - Answers to the Question "Is there a tech investment bubble right now?"

No clear Question Yes / Probably No answer

Is there a tech investment 8 3 1 bubble right now?

Most of the investors attributed the over-valuations less to hype, but rather to high liquidity in the investment market due to the low-interest politics of central banks around the world and few alternative investment opportunities:

"Interest rate was close to zero for many years, and the created a huge liquidity. Investors put great money into major funds and these major funds just bombard the funding into the market."

“There is a lot of capital that needs to be deployed within the economy and there are also public markets that drive up valuation levels.”

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 50 September 2016

“It's a hype combined with a lack of proper investment alternatives in the market, and that creates a different investment decision environment.”

When asked whether there was a “hype” on the market, some interviewees mentioned that there is actually a stronger hype for founding start-ups rather than for investing in them

"There is a hype where start-ups and technology ventures are becoming mainstream, and this goes into the media, and goes into corporates, and corporates want to participate in the whole thing, and the young people want to have an easy lifestyle, like winning the lottery, where you don't have to work a lot and you can win a huge sum."

“[People are] believing the hype and are having a rock star mentality, which I think... the celebration of entrepreneurship is great, but to turn that into the only decent alternative of life for people is wrong - because you need employees, you need people that support other people's projects.”

TABLE 4.12 - Parallels to the dot-com bubble

Mentions

High liquidity on market 8

General (Media) Hype around tech 5

Parallels Hype around tech entrepreneurship 5

No proper due diligence 2

Unclear/unsustainable business models 2

There were, however, several arguments on how the current situation is different from the dot-com period. One investor argued against such opinions and states that while he believes there is some overvaluation on the tech start-up market, generally speaking, there is no market bubble right now:

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 51 September 2016

"There are more customers, there are more well-educated entrepreneurs. The technological trend is clearly pointing in this direction that this is a growth sector for the decades to come."

It was also pointed out that the fact that there are far less publicly traded tech companies than at the dot-com bubble times shows that the market is a lot more controlled and less volatile than during the dot-com bubble. Furthermore, the market is currently mostly tackled by investors with more knowledge, caution and experience. It was additionally pointed out that a lot of the growth projections for companies during the dot-com period were based on the assumption that the internet would experience a much faster and more widespread adoption. Several interviewees said that they believed that now internet adoption reached levels where there is a consumer base around which massive internet businesses can sustainably be built.

TABLE 4.13 - Differences to the dot-com bubble Mentions

Fewer publicly traded young tech companies 4

Improved consumer base 4

Investors more sophisticated / Differences 4 knowledgeable

Investors more cautious 3

More substantial business models 2

4.8.3 Valuations of Unicorn Companies Generally, amongst the interview partners, there was a sense that at least some of the “unicorn” companies (companies valued at more than 1 billion USD) are highly overvalued.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 52 September 2016

TABLE 4.14 - Answers on the Question "Are current unicorn companies overvalued?"

Question Yes / Probably No No clear answer

Are current unicorn 8 4 0 companies overvalued?

The representatives of larger institutional investment funds stated, however, that valuations of many unicorns are misleading as all of these valuations are only on paper and more or less a result of negotiations between the company and its investors:

"Not many people really understand in a lot of detail how these valuation levels come together to create a unicorn valuation. All these valuations in a private round - this is all on paper. It doesn't really mean that this is a long-term stable valuation level. There is also an inherent risk in becoming a unicorn. You are applying a high valuation to your company and as long as you are not cash flow positive this can actually be quite hurtful as well."

Another institutional investor mentioned that in the case of an IPO, most unicorn companies would not be able to achieve the same value on the stock market as they currently have in their latest investment rounds:

"If you would translate those evaluations into normal stock lists in companies they would have much lower valuations, as those prices are really financially engineered. These companies might sometimes be "wrongly" prices, but in fact, it's actually a completely different pricing mechanism."

Generally, the opinions expressed about unicorn companies were rather negative. The following quote expresses a sentiment that many of the interviewees share:

“[For] Some of the more hyped cases in the world the day will come when they will be acquired or go public and personally I foresee that sky-high valuations - particularly above 10 billion - will be extremely hard to protect. Many others in the low range of 1 - 5 bn which I think are also just worth a fraction of that."

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 53 September 2016

It was additionally pointed out that the general hype around unicorns creates wrong and irrational incentives for founders as well:

"It misguides entrepreneurs to seek imaginary valuation hurdles. It skews some expectations on the entrepreneurs and creates wrong dynamics in growth rounds. Everyone who is now valued over 1 billion is now under scrutiny."

However, it was also emphasized that unicorns couldn’t be evaluated as a whole, but should be looked at on a case-by-case basis:

"Whether a company is valued 1 bn + can be totally justified if it has large potential or is a category-defining business in a large market with a great business model."

4.8.4 Lessons from the Dot-com Crisis The lessons the interviewed investors took from the dot-com crisis all revolve around being more careful in investments, asking for at least some substantial proof of the business case and making sure all relevant KPIs (“fundamentals”) are substantiated. Only one of the larger interviewed institutional investors mentioned that being more careful can also be a bit short-sighted. He explained that many business models developed in the late 1990s that were not sustainable back then, are in fact viable today and used by successful companies. Hence, besides doing proper due diligence, one should also consider that the timing for a business model needs to be right.

TABLE 4.15 - Lessons from the dot-com crisis

Mentioned Lessons Mentions

More caution / due diligence 6

Require early validation of business case 3

Focus on fundamentals 2

Timing matters 1

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 54 September 2016

4.9 General Findings

4.9.1 Decision-making Factors in Context While the interviewees generally rated almost all the factors presented as similarly important for investment decisions, a qualitative analysis of the answers (examining people rating something as “a six on a scale of one to five” or making statements like “this is the most important factor”) shows that the general approach of investors seems to be to emphasize two things the most: the founder team and the business model. Further analysis of mentions shows that product engagement KPIs, early revenue and LTV indicators being seen as very important (see tables 4.2 and 4.5), but - as the qualitative analysis of the interview revealed - not always mandatory factors for investment decisions.

While business models were seen as mutable, the team was pointed out as the single most important factor as it was harder to change and could adapt the company’s focus if needed. As one investor stated:

“With a good team, you will always find a market”.

Vice versa another investor mentioned that a bad or average team could make a company fail despite a great market opportunity and business model.

4.9.2 Case-by-Case Evaluations Almost half of the interviewed participants pointed out at some point of the interview that many start-up evaluation cases need to be looked at on an individual basis (“it’s a case by case decision”, “it depends”) and argued that it was impossible to have one single process or model for investment decision-making. Some of the investors mentioned that they have distinct approaches for certain business models and certain market types (“Software as a service” was mentioned by several participants as a business model with a distinct approach to valuation associated with it), but this was not explored further in this research. One institutional investor explicitly referred to the case-by-case assessment towards the end of the interview:

"I really want to stress that there is hardly any kind of standardized process; sure there are some standardized KPIs that I really look into - I check any investment opportunity against a set of 10 KPIs - and if they are being met I start doing work, but after that it's a case by case assessment"

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 55 September 2016

This study did not aim to probe deeper into valuation methods for specific industries or business models. Further research into such areas can yield valuable additional insights.

4.9.3 Reliance of Institutional Investors on Experts and Angel Investors Generally, representatives of institutional investment funds mentioned a reliance on experts for evaluating many of the factors addressed in this research, like evaluating markets, technologies, business models or the founder team. Additionally, while the interviewed Angel Investors all pointed out that they invest mainly in pre-seed or seed stage funding rounds, some of the institutional investors mentioned that they are often relying on the due diligence done by such angels as a basis for their own analysis. Such an interplay between angel investors in the (pre-)seed round and institutional investors did not exist to such extent in the investment landscape of the late 1990s as it is not mentioned in any of the research on the dot-com bubble.

4.9.4 Involvement of Investors with Founder Team While institutional investors referred often to the resources, networks and expertise they could bring to a start-up, angel investors frequently referred to their motivation to get involved more strongly in the start-ups:

“I have to be convinced with the idea, I have to be convinced that the numbers add up and make sense, so 50/50, and at the end of the day, I really want to work with this start-up, or with the company I am investing, saying that I really want to be there as an adviser, I don't want to be just giving money and then is a fire-and- forget"

Further hints of wishing to work more closely with the team are the use of phrases like “Chemistry with the founders” or “I want to use the product myself”. One could argue that this indicates a larger emotional component in investment decisions than for institutional investors.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 56 September 2016

Chapter 5: Conclusions

5.1 Introduction

The aim of this chapter is to give a summary of the research findings, answer the research questions, point out additional notable conclusions and discuss how this research advances the current research in this area. Furthermore towards the end of this chapter the limitations of this research will be outlined and additional areas for research recommended.

5.2 Findings Summary

This dissertation set out to examine the behaviour of tech start-up investors and to compare it to the behaviour of investors during the dot-com bubble period. This was done in order to identify lessons learned, mistakes that are being repeated and understand if in the current situation there are indicators that the tech start-up investment market is steering towards another financial bubble.

5.3 Answering the Research Questions

This research aimed to answer the following questions: ● Which lessons did investors take from the dot-com crisis? ● Which mistakes are investors repeating from the dot-com crisis? ● Are there indications that the behaviours of investors could steer the tech investment market towards financial crash?

In order to gather data on these questions, 12 investors from 8 countries were interviewed to gain an understanding of their current investment strategies and their own view on lessons they learned and on a potential bubble in the market. For drawing the conclusions both the explicitly stated answers to the questions and answers inferred by comparing decision criteria and stated behaviours with those identified in the research (see appendices A, B and C) are taken into consideration.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 57 September 2016

Research-Question 1: In which aspects do technology Startup Investors follow Lessons and Recommendations from the Research on the Dot-com Crisis in their Investment Strategies in 2016?

This research shows that there are a number of lessons and improvements investors are applying in their investment strategies compared to investors during the dot-com bubble period.

The key lessons and improvements identified are ● A more cautious approach: Even though some hype exists on the market currently, most investors indicated generally bigger caution in their investments and an emphasis on extensive due diligence even when something is considered to be a “sure bet”. ● Acknowledgement of the limited use of the profit KPI: Rather than forcing the profits indicator and projections into an evaluation model as it was attempted often during the dot-com bubble period, investors have accepted that earnings are hard to estimate and predict at early stages of a company and rather emphasize different factors for their investment decisions. ● Validating business models: while there is (similarly to the dot-com period) a strong emphasis on the business model when evaluating a tech company, contemporary investors are often not satisfied with purely theoretical considerations and demand a validation of the business case a company claims. ● Linking engagement KPIs to profit: While during dot-com bubble times investors were satisfied with high engagement rates like website hits and assumed that those will easily translate into profit, investors nowadays are examining engagement KPIs in the context of the entire business model and require special emphasis on factors like stickiness and a clear funnel towards profit. ● More realism in market expectations: While expectations towards the performance of tech start-ups are still very high, the underlying fundamental assumptions about the global adoption of the baseline technology (the internet) and the maturity of markets are more realistic now. However, some investors hinted towards "pockets of irrational investments" within tech investment. Identifying and examining such areas would be a suitable topic for further research. ● More realistic expectations towards competition: While during the dot-com bubble period competition was implicitly assumed to be favourable, during this

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 58 September 2016

research competition was explicitly mentioned as always increasing the more successful a start-up would get. There is the caveat however that increased competition does not seem to play a role in the modelling of an evaluated company’s development, which is hinting towards an implicitly assumed favourable development. ● More experienced and knowledgeable investors: While institutional investors still generally had a background in business and the financial industry, angel investors tend to have a tech background. Institutional investors rely in some parts on the evaluation and expertise of these angel investors. As angel investors did not exist in this form and quantity, it can be assumed that their emergency generally increased investment decision quality. Where investors find their own knowledge lacking, they regularly commit themselves to an in-depth research and involving outside advisors rather than purely trusting their intuition and limited own expertise.

Research Question 2: Which Mistakes done during the Dot-com Bubble Period are technology Start-up Investors repeating in 2016?

While investors did improve their investment processes in some areas significantly, there are some areas in which they show similarly risky behaviours as investors during the dot- com bubble period.

Potentially risky behaviours identified are: ● Institutional investors mainly have financial industry background: While angel investors in this research were shown to have experience and education related to the tech industry, most institutional investors in this research did not. As institutional investors carry out larger venture capital investments, there might still be a risk of lower quality decisions on their side due to a lack of understanding combined with volumes high enough to impact the market significantly. This was similar during the dot-com bubble time period (Briginshaw, 2000). ● No clear goals for exits: generally the exit potential of tech start-ups is not taken into consideration by all the interviewed investors and amongst those who do there is often no clear goal at which they would try to exit. This was similar in the dot- com period (Chang et. al., 2016).

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 59 September 2016

● Favourable implicit assumptions about costs: While costs, CAC and cash burn rate do play a role in investment decisions, they are seen as secondary to factors like revenue or the quality of the founder team. Also, costs were assumed to increase linearly or even decrease as a company scales up operations. These assumptions appear to be similar to those during the dot-com bubble period (Ofek and Richardson, 2002). ● Strong initial focus on revenue development: The strong focus on revenue potential, and a (at least initial) disregard of profits and only considering cost or CAC as supportive KPIs indicates a potential danger of creating valuations with a high inherent price to revenue ratio, which is seen in the research on the dot-com crisis as an indicator for a bubble (Griffin et. al., 2011). ● Favourable implicit assumptions about competition: Even though it was explicitly stated that increased competition was seen as inevitable, it was never stated that this assumption was included in the modelling of the company beyond a binary check if a product was easy to copy.

Research-Question 3: Are there indications that the behaviours of investors could steer the tech investment market towards another financial crash?

As every single interviewee commented on the possibility of another tech bubble building in the current situation, this topic was emphasized more strongly than originally anticipated in this dissertation. As pointed out in the literature review on the dot-com crisis, it is impossible to say with certainty during a price hike of assets like tech companies if this is a sign of a bubble (Siegel, 2003). Hence this research can only provide an overview of possible signs that the current situation might be a tech bubble additionally to the potential mistakes pointed out above.

According to this research there are a number of indicators that there might be a tech bubble currently:

• High Pressure to invest due to High liquidity on the financial markets: With currently low interest rates and few investment alternatives, there is increased pressure to invest money in tech companies and reach very high payouts while having increased competition with other investors. This leads to higher, and potentially too high, valuations and investments that would not take place with less investment pressure from the financial markets.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 60 September 2016

• Investors failing to make good valuations: The majority of interviewees judged a majority of current valuations and investment decisions on the market as problematic. This distorts and drives up valuations further. • Overvaluation of unicorn companies: Most unicorn companies that appeared in the past years seem to be overvalued and interviewees do not expect most of them to realize these valuations were they to go into the public market. This is likely the largest risk factor in the tech investment market currently as a massive devaluation of these companies might send a strong signal to the venture capital market and financial areas beyond it. • Investors believe a tech bubble is building: While it is difficult to truly pinpoint the build-up of a bubble, the majority of interviewees were concerned that it is currently happening. • A stronger focus on softer factors is riskier: In their hunt for catching great companies as early as possible in their development, investors put in their evaluation increased focus on factors that are not or not easy to quantify, which increases the possibility of irrational, emotional decisions. • Case-by-case evaluations introduce systemic risk: Most investors perform case-by-case evaluations and only few seem to try to create a general set of investment models or principles and challenge existing ones. One could argue that this might make it more difficult to identify moments when the entire tech investment market shifts towards more problematic behaviours. That is because without any pre-defined boundaries in principles or models it might be difficult to identify whether the whole market is moving closer to or crossing these boundaries.

5.4 Summary of Conclusions

Overall tech investors evolved and seem to be better equipped to properly evaluate tech start-ups, put more emphasis on more meaningful factors in their evaluations and are more cautious in their general approaches. There are some potentially risky behaviours similar to ones identified in the dot-com bubble period, but one can argue that they might be offset if not entirely, so at least enough to prevent a complete financial market crash like when the dot-com bubble burst. Especially the fact that the risk is largely contained within the professional investment space decreases the risk of a devastating tech market

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 61 September 2016

crash sending shockwaves to all other financial markets and the global economy in general.

Additionally, while each of the mentioned factors for a building tech bubble might have a negative impact on the financial market situations, they are not necessarily indicators that such a tech bubble is really present. It seems very likely that (in some cases substantial) devaluations and less favourable investment terms for start-ups will manifest in the near future. However, at this point, it seems unlikely that this will lead to a general investor panic and rapid and total withdrawal of funds across the entire venture capital market as there are no viable investment alternatives. Also, business models of many start-ups are set up well enough to survive a prolonged period of time or even generate profits after a cost-cut in case this becomes necessary. The times of accessing limitless money supplies to fuel rapid growth will then, however, be over. If there is a bubble, its deflation can be expected to mainly hit venture capitalists, large tech companies and especially unicorns hard and make it more difficult to acquire funds as a tech start-up in some markets, but the impact on the general public is likely to be a lot less disastrous as the dot-com bubble burst was.

5.5 Relevance of Research Question

The gained insights provide a valuable perspective on investment strategies across the tech market and many starting points for researchers to dig deeper into for evaluating and predicting the development of the financial tech investment market. Furthermore, investors can use these insights to examine, deconstruct and refine their investment strategies and make sure they are not unintentionally driving themselves and the entire market into unsubstantiated over-valuations. Lastly, start-up founders can use the insights of this research for evaluating their start-up from the perspective of investors and for preparations for raising funding for their company.

5.6 Limitations of Research

The conclusions of this research are based on the collection and analysis of interviews carried out for this research. In the process of gathering, analysing, summarizing and codifying the gathered data, certain assumptions and compromises were made that lead to a number of limitations. These limitations are:

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 62 September 2016

5.6.1 Sample Size and Composition A total of 12 investors were interviewed for this research. As most of them were from Europe and mostly only covered early stage investments rather than the entire investment cycle for tech start-up investments, the conclusions have only limited applicability for the entire tech investment market. This becomes especially evident when observing unicorn investments since those - as pointed out by some of the interviewees - seem to operate under very different investment decision frameworks and principles. Another hint towards this is that the one interviewed late stage investor actually does consider profits and historic data as very important and is applying more classical financial analysis models compared to the rest of the interviewed investors.

5.6.2 Market Differentiation While the dot-com bubble was all about internet-based companies, nowadays the term “tech company” can be applied to ventures in a number of different markets, some of which seem to exhibit more “bubbly” patterns like less knowledgeable investors, unclear business models, etc. Due to this and the mentioned “case-by-case” decision making amongst the interviewed investors, investment decisions in one market might be less sound than in another even for the same investor.

5.6.3 Broad Research Topic Since the research questions of this study are very broad, many aspects of investment strategies could only be touched in a general way. While the aim was to identify general investment strategies, it seems that examining strategies for specific markets, business models or technologies could yield more substantial and structured information.

5.6.4 Public vs. Private Companies A large portion of the research on the dot-com bubble period revolves around publicly traded companies. In the current market situation, there are hardly any publicly traded tech start-ups, so this research focussed on privately funded companies to gain insights into a - at the time of writing - more relevant section of the tech start-up investment market. This does, however, make the direct comparison of behaviours problematic, as the data on investments from the dot-com bubble period includes a larger amount of small, individual investors whereas in the current situation most investors are professionals.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 63 September 2016

5.7 Recommendations for Future Research

There are a number of future research areas that can be undertaken as a follow up to this dissertation. Firstly, the topic should be examined additionally by doing a quantitative analysis of the market movements with a specific focus on investor behaviours and assumptions, secondly interviews and surveys should be carried out with a much larger sample of participants to get a more representative image of the situation across investor categories and geographies. Besides extending the sample, the focus should also be set more narrow and researches of this kind undertaken to examine specific start-up markets, geographies or technologies. As the importance of the founder team was emphasized so heavily by the investors in this research, it would be valuable to explore both how exactly founders are evaluated and how positive and negative evaluations in this regard compare to the actual later performance of the start-up. Lastly, unicorn companies should be examined and a comparison of valuations, KPIs and financials with those of dot-com bubble companies that went bust carried out in order to identify those companies with the biggest danger of imploding and bringing the market down in the process.

5.8 Closing Remarks

This dissertation set out to explore how tech start-up investor behaviours changed in comparison to the behaviours of investors during the dot-com bubble period. It also explored parallels and differences of the current situation on the tech investment market compared to the investment market during the dot-com bubble period. The results indicate that there are both clear continuities and differences in the behaviours of investors and that investment strategies improved over the past decades since the burst of the dot-com bubble. However, there are still risky behaviours and some indicators that a tech bubble or at least a certain level of overvaluation of tech companies might currently exist on the market. While investors are aware of the risks of start-up investing, even at too high valuations, the potential payout both for them and society in general are large. When asked about reasons for his risk-friendly start-up investment strategy, one of the interviewees mentioned that the only way to achieve extremely high investment pay-outs at this point was to spread out investments amongst as many promising start-ups as possible despite the high uncertainty this involves. He referred to a quote from the book “The Black Swan” and mentioned that it summarizes the attitude of most venture capitalists:

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 64 September 2016

“The strategy for discoverers and entrepreneurs is to rely less on top-down planning and focus on maximum tinkering and recognizing opportunities when they present themselves. So I disagree with the followers of Marx and those of Adam Smith: the reason for free markets work is because they allow people to be lucky, thanks to aggressive trial and error, not by giving rewards or “incentives” for skill. The strategy is, then, to tinker as much as possible and try to collect as many Black Swan opportunities as you can.” (Taleb, 2007, p. xxv)

In the spirit of this quote, one could argue that failing tech investments and even start-up investment bubbles are necessary evils on the path of achieving economic progress.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 65 September 2016

References Bell, S., 1996. Learning with information systems: Learning cycles in information systems development. New York: Routledge.

Brunnermeier, M. K., & Nagel, S., 2004. Hedge funds and the technology bubble. Journal of Finance, 59(5), 2013–2040.

Chang, V., Newman, R., Walters, R. J., & Wills, G. B., 2016. Review of economic bubbles. International Journal of Information Management 36(4), 497–506.

Chen, W. & Hirschheim, R., 2004. A paradigmatic and methodological examination of information systems research from 1991 to 2001. Information Systems Journal, 14(3), pp.197-235.

Cohan, Peter, 2016. 7 Ways 2016's Tech Bubble Differs From The Dot-Com Disaster. Forbes.com. [online] Available at http://www.forbes.com/sites/petercohan/2016/02/12/7- ways-2016s-tech-bubble-differs-from-the-dot-com-disaster/#2faead5639b9 [Accessed 24 July 2016].

Cooper, M. J., Dimitrov, O., and Raghavendra Rau, P., 2001. A Rose.com by Any Other Name. Journal of Finance ,56 , p. 2371–88.

Corr, T. A., 2006. Boom, Bust, Boom: Internet Company Valuations - From Netscape to Google. Financier, 13/14, 66–84..

Crabtree, B.F. and Miller, W.L. eds., 1999. Doing qualitative research. Sage Publications. pp. 127-138.

Cusumano, M. a., October 2013. Evaluating a startup venture. Communications of the ACM, Vol. 56 Issue 10, p. 26-29.

Eatwell, J., Milgate, M. and Newman, P. (eds), 1987. The New Palgrave: a Dictionary of Economics. New York, Stockton, p. 281.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 66 September 2016

Eli Ofek and Matthew Richardson, 2002. Valuation and market rationality of internet stock prices. Oxford Review of Economic Policy, vol. 18(3), pp. 265-287.

Elnathan, D, Gavious, I, & Hauser. S., 2010. An analysis of private versus public firm valuations and the contribution of financial experts. International Journal Of Accounting, 45, pp. 387-412.

Gavious Ilanit and Schwartz Dafna, 2010. Market valuations of start-up ventures around the technology bubble. International Small Business Journal, vol. 29(4), pp. 399–415.

Geier, Ben, 2016. Tech Stocks and the 2000 Dotcom Bust: 15 Years of Wall Street Lessons. Time.com. [online] Available at http://time.com/3741681/2000-dotcom-stock- bust/ [Accessed 31 August 2016].

Gollotto Jamie C. and Sungsoo Kim, 2004. Market valuation of dot com companies; R&D versus hype. Managerial Finance, vol. 29(11), pp. 61-72.

Google Finance, [online] Available at www.google.com/finance [Accessed 26 June 2016].

Griffin John M., Jeffrey H. Harris, Tao Shu and Selim Topaloglu, 2011. Who Drove and Burst the Tech Bubble?. The Journal Of Finance, vol. LXVI (4), pp. 1251-1290.

Higson, C., & Briginshaw, J., 2000. Valuing Internet Businesses. Business Strategy Review, 11(1), 10.

Howcroft, D., 2001. After the goldrush: Deconstructing the myths of the dot.Com market. Journal of Information Technology, 16(4), 195–204.

Hsu, D. K., Haynie, J. M., Simmons, S. a, & McKelvie, A., 2014. What matters, matters differently: a conjoint analysis of the decision policies of angel and venture capital investors. Venture Capital, 16(1), pp. 1–25.

Hudson, E., & Evans, M., 2005. A Review of Research into Venture Capitalists’ Decision Making: Implications for Entrepreneurs, Venture Capitalists and Researchers. Journal of Economic and Social Policy, 10(1), Article 3 (1-18).

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 67 September 2016

Ibrahim, D. M., 2008. The (Not So) Puzzling Behavior of Angel Investors. Vanderbilt Law Review 61 (5): 1405–1452.

Kettell Brian, 2002. Valuation of Internet and Technology Stocks. Butterworth Heinemann.

Khanin, D.; Baum, J.R.; Mahto, R.V., Heller, C., 2008. Venture Capitalists’ Investment Criteria: 40 Years of Research. Small Business Institute® Research Review, Volume 35, p. 187-192.

Lashinsky, Adam, 2016. The truth behind Unicorn valuations. Fortune.com. [online] Available at http://fortune.com/2015/10/22/startup-unicorn-valuation-uber-real-worth/ [Accessed 31 August 2016].

Leone, V., & de Medeiros, O. R., 2015. Signalling the Dotcom bubble: A multiple changes in persistence approach. The Quarterly Review of Economics and Finance, 55, p. 77–86.

MacMillan, I. C., Siegel, R. and Subba Narasimha, P. N., 1985. Criteria used by venture capitalists to evaluate new venture proposals. Journal of Business Venturing, (1): 119 – 128.

Mahmood, Tallat, 2015. The Tech Industry Is In Denial, But The Bubble Is About To Burst. TechCrunch. [online] Available at https://techcrunch.com/2015/06/26/the-tech-industry-is- in-denial-but-the-bubble-is-about-to-burst/ [Accessed 25 July 2016].

Mills R., 1998. How do you value a start-up company. Long Range Planning, vol. 31(3), pp. 446-454

Mims, Christopher, 2016. This Tech Bubble Is Bursting. Wall Street Journal. [online] Available at http://www.wsj.com/articles/this-tech-bubble-is-bursting-1462161662 [Accessed 25 July 2016]

Mingers, J., Mutch, A., & Willcocks, L., 2013. Critical Realism in Information Systems Research. MIS Quarterly, 37(3), 795–802.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 68 September 2016

Morris, J.J.; Alam, P., 2012. Value relevance and the dot-com bubble of the 1990s. The Quarterly Review of Economics and Finance, 52, p. 243–255

Nathan, Allison, 2016. Robert Shiller: Unlike 1929 This Time Everything - Stocks, Bonds And Housing - Is Overvalued | Zero Hedge. Zerohedge.com, [online] Available at http://www.zerohedge.com/news/2015-05-29/robert-shiller-unlike-1929-time-everything- stocks-bonds-and-housing-overvalued [Accessed 31 July 2016]

Ning, Y., Wang, W., & Yu, B., 2014. The driving forces of venture capital investments. Small Business Economics, 44(2), pp. 315–344

Orlikowski, W. J., Baroudi, J. J., & Orlikowski, W. J. , 1991. Studying Information Technology in Organizations: Research Approaches and Assumptions. Business, 2(1), 1– 31.

Peter Curwen , 2016. Double bubble trouble: is history repeating itself again?. info, Vol. 18 Iss: 1

Pinsonneault, A., & Kraemer, K. L., 1993. Survey research methodology in management information systems: An assessment. Journal of Management Information Systems, 10, p. 75-105.

Press, Gil , 2016. Yahoo IPO And The Birth Of Computer Programming: This Week In Tech History. Forbes.com. [online] Available at http://www.forbes.com/sites/gilpress/2016/04/11/yahoo-ipo-and-the-birth-of-computer- programming-this-week-in-tech-history/ [Accessed 31 August 2016]

Robert J. Hendershott, 2014. Net value: wealth creation (and destruction) during the internet boom. Journal of Corporate Finance, vol. 10, pp. 281– 299)

Romanova Natalie, Marilyn M., Helms Margaret Takeda, 2012. Valuing internet companies: A more accurate, comprehensive financial model. Academy of Entrepreneurship Journal, vol. 18 (1), pp. 35-53

Rosser, J. B., Jr., 2000. From Catastrophe to Chaos: a General Theory of Economic Discontinuities. Kluwer Academic, 2nd ed.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 69 September 2016

Saunders, M., Lewis, P. and Thornhill, A. 2009. Research Methods for Business Students. Harlow: Pearson Education

Schwartz, E. S., & Moon, M., 2000. Rational Pricing of Internet Companies. Financial Analysts Journal.

Shiller, Robert, 2005. Irrational exuberance, Second Edition, Princeton: Princeton University Press, pp. 113 - 147

Sieber, S.D., 1973. The integration of fieldwork and survey methods. American journal of sociology, pp.1335-1359.

Siegel, J. J., 2003. What Is an Asset Price Bubble? An Operational Definition. European Financial Management, 9(1), 11–24.

Silverman, D., 2007. A Very Short, Fairly Interesting and Reasonably Cheap Book about Qualitative Research. London: Sage.

Singh, H., Aggarwal, R., & Cojuharenco, I.,2015. STRIKE A HAPPY MEDIUM: THE EFFECT OF IT KNOWLEDGE ON VENTURE CAPITALISTS’ OVERCONFIDENCE IN IT INVESTMENTS. MIS Quarterly, 39(4), 887–908

Taleb, N.N., 2007. The black swan: The impact of the highly improbable. Random house.

Valliere, D., & Peterson, R., 2004. Inflating the bubble: examining dot-com investor behaviour. Venture Capital, 6(1), p. 1–22.

Van Gorp, N. & Honnefelder, S., 2015. Regulation and Competition: Challenges for Competition Policy in the Digitalised Economy. Communications And Strategies, 99, pp. 149-162

Vardi, M. Y. , 2016. Are we headed toward another global tech bust?. Communications of the ACM, 59(4), p. 5.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 70 September 2016

Walsham, G., 2006. Doing interpretive research. European Journal of Information Systems. 15(3), 320–330.

Wang, L., & Wang, S., 2013. Venture Capital Backing and Overvaluation: Evidence from the High-Tech Bubble. Financial Review, 48(2), 283–310.

Yin, R.K., 2003. Case Study Research: Design and Methods. 3rd edn., Thousand Oaks, CA: Sage

Zeng, Y., 2016. Institutional investors: Arbitrageurs or rational trend chasers. International Review of Financial Analysis, 45, p. 240–262.

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 71 September 2016

Appendices

Appendix A: Abridged & Anonymised Full Analysis Participants 1-4

Participant 1 Participant 2 Participant 3 Participant 4

Context VC Angel Angel VC

Experience Financial Industry Legal & Finance Tech Industry Finance

Business Admin & Computer Education Business Administration Law Science Business Administration Angel investor

Investment Manager, government- very early startups, seed or pre- Person & Org funded VC fund Super-Angel, Angel Fund seed VC fund / micro-VC

100k - 1 mio / round, up to 3 mio Investment Volumes total / company Euro 250k - 750 k $ 10k - 60k British Pounds 200k - 400k Euro Europe North America / Western Geographic Focus specific European country Europe Competition in US too high generally Europe

Industry Focus Any Technology Tech industry Tech industry

General Only passively (wants to get approached)

Initial due diligence, then call w. pitch

Legally restricted to one country Step 1: Proposition, USP, market, competition High-growth company in tech field Prepared to ask questions, Do not fund product development or challenge them, test how well- prototyping, only later when Emphasis on the founder team thought through it is Team, validated/market proven product --> industry experience, Technology, exists understanding of the user, Step 2: accounting perspective - competitive advantage (as US build a platform for this user burn rate, how to use money, companies usually faster, but Usually come in as second or third cash flows, KPIs - Revenue, not Hungarian ones are cheaper) investor SAAS: Prefer already profit matters existing/launched product Able to help the company Like: Digitalizing a fragmented Do they want a fitting investor or market; clear markets, clear value No need for many customers, just money? They need to want Market Understanding & Exit Investment Strategy proposition but should have a few to work with the investors options Very liquid market

Could be that valuations might get corrected just the same, but will not impact normal people as much due Parallels to Dot-com to companies not going IPO as High liquidity, too much money Lots of money, lots of artificial times much in the market inflation of company value N/A 1) Less aggressive investments A vision is not enough to raise money

Need a prototype for seed funding Need market & traction data for series A funding

Majority of dot-com companies wouldn't get funded now

2) Less IPOs dot-com: lots of IPOs, now: very Not as many listed companies --> few will not impact retail investors, Reason: less volatile capital closed loop within investor setting generation for the company & investors; if investors tried to Did not understand the (financial) cash out at once in an IPO, the Back then companies had no tech market & did not understand stock and potentially the market real value, no work to show for fundamentals of tech companies Valuations more reasonable, would crash the valuation more substantiated Back then people thought new era This way cutting valuations The majority of craze happened Differences to Dot-com of digital ventures, but only now Better understanding of does not cause a chain reaction in the US market back then, times gone into full swing products, measurements on the market same now Fundamentals are key There is no general bubble, it's more differentiated Business model needs to be proven Be less aggressive in investing, Lessons taken from to monetize (at least on small scale) demand valid data not just a Valuations should have some Dot-com times N/A vision connection to fundamentals

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 72 September 2016

More cautious, middle path

Know How

Market Knowledge Importance Rating 5 3 4 4 Depends on the kind of investor - as an angel it is important, because so early in the Market mechanisms & sales cycles company life need to be understood In some industries you can Series A/B - not so important as invest with little knowledge and Market Knowledge Impossible to invest without Need general understanding of already proven market, product- in some it matters a lot; Importance Reasoning understanding the market the market, but not in depth market fit generally it's important to have it

Own Market Knowledge 4 4 5 4 Individual desktop research

Network of industry experts (from state) - 200 experts from every industry

Database of 1200 pitches

Benchmarking different products 3-5 (averaged to 4) Some just average, others Market Knowledge Learning every day, evolution of much better since being a Was working in the fields he is explanation fund strategy traveller himself invested in

Knowledge of technology/tech trend importance rating 3 5 5 5 Due diligence in this is "Trend" is the important word important; if lack of insight, evaluate the technical skills of Investing in startups is VCs need to be ahead of the the lead engineer/CTO investment in the future, no curve short-term gain Technology can come and go, it's no need to understand the (was a 7, but that was not an Explanation all about the market code itself option) Own Knowledge of technology/tech trend rating 4 4.5 4 2.5 Nobody technical in the VC; generally European VCs are investment bankers

US more engineers turned entrepreneurs turned VC

Q: IS THAT A DISADVANTAGE? Yes, not as good in forseeing things if you lack knowledge

Own Knowledge of These are the things that, if you technology/tech are not in technology, you don't reasoning Have really good know how N/A see, simple as it is. Combination of different methods; Big companies going after a not easy to find disruptive new ones trend - Google, Facebook, if you want to invest early as Apple noboda heard of them yet They are good indicators Industry experts

How to assess tech Invite Startups in very new trend to Otherwise industry reports, Try to stick to one trend to get trend discuss & assess No simple answer; intuition analyst reports network effects

Evaluation Criteria Reduce revenue projections, increase cost projections

Evaluate market transaction costs & prices

Discount planned exit point back to today for equity value General financial setup Cash Flow, burn rate, expense Sometimes use Discounted future structure cash flow method Does the budget & forecast Terms sheet important - liquidation make sense? preference or other protective Discounted Cash Flow General Financial mechanisms --> higher price; Not just looking at KPIs, but the Method or model payout in tranches --> higher price No, case by case underlying assumptions

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 73 September 2016

Depends on business model

Market Place: Sales funnel KPIs - traffic, Customer Acquisition costs (CAC), marketing spend for conversion, how big the market is At least some paying Financial KPIs Of course Revenue and IBTA customers N/A Should be profitable on a transaction basis

"That's probably the most reliable source of truth. If you're profitable How to evaluate on a monthly basis, by given, I'd business model say, the business model works."

Customer Lifetime- Value importance rating 4 5 3.5 5 Only assumptions in seed stage

Analyse customers, see if they later important how it actually are locked in worked and IF it actually works, Customer Lifetime- always compare CAC with LTV Value importance That's why preference for 2.5 in seed No other way to define reason First time CAC, recurring buy rate SAAS, high lock in rate 4 in series A/B revenues

Market Opportunity rating 5 3 5 3 More looking for narrow niche The bigger the market opportunity, the better or company that can become Market Opportunity (Originally rating was 6, but that one of the bigger, but not reasoning was not an option) biggest player Reading Analyst reports Checking if big players going in Potential for growth and market that direction leadership so the company How to assess market Top down and bottom up research becomes interesting for an exit Early markets are good, only Desk Research and information opportunity (see above) or IPO small % finished from network/experts In Europe, most look at profitability

In Anglo-American area, nobody Most come with hockey stick does; revenue is important, scenario shows people are willing to pay Invest in tranches bound to SIDE-TRACK: WHAT ABOUT milestones, so the plans need TWITTER? to be realistic Evaluate Sales Plan, assign Very late stage startup, at a probabilities stage where they should make If dynamic or not depends on profit already; startups should the business model - some Revenue/profit dev Evaluate entire sales pipeline - Lots of Q&A w. founders; have a strategy of how to make need rather a look at number of dynamic? realistic? Intuition & guesswork money customers for example Sometimes qualitative, sometimes quantitative Set hypothesis on size of User testing at due diligence Show in small niche that it is market, competition, etc. based working on experience and input from For consumer products also experts Estimate future engagement KPIs - monthly/daily HAS to have customers & demand? active, downloads, ... N/A market opportunity Experienced founders that inspire confidence

Don't do super early investments, And/Or prototype and first want to see few months of customers development Analyse first customers (are they representative) and Need to have a proper model Sometimes just work with Don't want a lot of data, but do want project based on their set up; if a founder is unable to company for a few months Lack of historic data? SOME data behaviour & total market size do that, he will fail for sure before investing in them

Competition rating 5 5 3 3

If there is a trend, there is Competition reasoning N/A competition If big player like Google working on Test competition himself similar feature, then very important factor (factor in with 5) Q&A w. founders

How to evaluate Otherwise factor it in with a 3 - if Sometimes 3rd party expert to Clearly understand the USP and Market maturity, competitors, competition enough space left in the market evaluate differentiator state of the market generally Don't focus on earnings initially anyway

Market share is more important, profitability will follow More important to understand Everything takes longer and is more the market, as you can predict expensive than planned However should not rest too maximum 18 months Problems with long on market share, evaluating future Stretch assumptions out, increase eventually it has to become Need at least some customer Don't care about it in seed and earnings? cost, plan reserves profitable data, otherwise won't invest even in series A

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 74 September 2016

From investor side: If you can't do it properly, you put protective measures in place like liquidation preference, anti-dillution mechanism, preference shares, voting rights Risk lies in losing out on Due diligence, summary of all alternative investments From company side: Reduce costs factors - founders team, There is always a risk; to buy time to mitigate failure, market, competition --> get everything mentioned above Risk is in companies staying Evaluating risk relaunch product, ... better intuition also aims at the risk mediocre Benchmark against collection of exits

Different scenarios: acquihire, Not doing it, too early stage market access, technology Benchmark against other exits in investments the space & match with Try to categorize where a Evaluate exit assumptions in business case - Analyse potential acquirers, Doesn't like founders who aim company could fall into and scenarios? factor those in other exits in the space at exit right away then optimize towards that exit

Non-Financial Criteria Single founder --> risk, 3 --> good Paying customers

Transparency in reporting & comms User/Customer Churn User base, contracts, Execution - roadmap, deadlines Monthly/Daily Active Users partnerships kept, being realistic, decisive, Customer / User Acquisition General keeping direction Trends in data Engagement numbers, DAU founders

R&D Rating N/A (skipped) 5 2 3

R&D Reasoning N/A (skipped) too early N/A

How factoring R&D in N/A (skipped) N/A Depends on the product, generally number of users and how often they do the core action

Check how much growth is Engagement KPIs? (See above) see above fueled by marketing (expenses)

Basis for competitiveness

Would I use it myself? Not need to understand to the Try to use it himself; try to Value proposition and vision very basics Understanding understand the problem it needs to be very clearly Product/Service? resolves understood Analysing customer feedback Should be profitable on a transaction basis Key questions: Can it be monetized, how, why would Trial and error - continues after "That's probably the most reliable customers pay, would it stick? Need to understand the market the investment source of truth. If you're profitable opportunity and how the Understanding on a monthly basis, by given, I'd Thorough evaluation of business model fits, if the Pivot often in seed stage, so it's Business Model? say, the business model works." replicability & competitiveness funnels make sense ok

Importance of Founder Team? 5 5 5 5 Single most important factor

If management is strong, they will figure out how to adapt a The earlier the stage, the more Explain importance product, attract talent etc. important Resume, recommendations, industry experience, successes Talk with senior team about founders - if they say different Really subjective, spend time Experienced, but not too much, so things, there is something with them in the office they are still hungry & flexible wrong --> Mood, consistency, dynamics Should really understand the Many conversations Experiences generally and in competition industry WHY DO SHARES MATTER? How evaluate Founder Understanding of market & Motivation, dynamics between Team? How they function together competition them, contribution reflected Importance of intangible IT assets/capabilities? 5 3 5 5 Only need to be good

Strong leadership will attract Tech founder has to be a rock Key talent to innovate and Why? N/A great talent star disrupt, need superior skills Look for scalability, technology How evaluate intangible stack, reliance on certain IT assets? N/A N/A technologies

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 75 September 2016

Importance of networks 4 2 2 4 It's all about the leader; if CEO is right person tis can be fixed by investors Comes from investors Not that vital, but helps for Main benefit is to get in understanding the industry and Why? So they learn from other founders Investors bring the networks in additional team members tech trends

How evaluate? Needs to be global N/A N/A

Learning Ultimately if there is a strong gut feel for not doing something, For less clear decisions; enhances Very important - making an follow it decisions educated guess Trust first impression Founders team evaluation a lot Should fall in love with the venture 4-5 Intuition is important when you on gut feel and experiences Intuition, gut feeling, have extensive knowledge and personal experiences? 5 Founders, markets, exit experience in the area Technology trends investment committee, which consists of 5 very experienced investors and entrepreneurs; raises questions and red flags

always 2 investors for one investment, then check w. director Try to identify inconsistencies and risk management or mismatches in data and models Also check w. industry experts Conversation with founders on Looking at big picture; needs to Checked by everyone in the How to avoid errors? lots of eyes looking over it assumptions be aligned and make sense company (6 people) No Standard set of questions and topics Meet the company, ask for one- pager, ask for PG deck & financial plan

Then Validation phase - talk to How adapt evaluation customers, partners, investors, models? No No No team, experts Conversation; base hypotheses Locked in as an investor built need to be reevaluated (e.g. market size) Work with management to Support where possible improve things Then adjust - train founder? Pivot product? If market is What do they do if their If nothing helps, try to protect their If management doesn't listen -- wrong/bad, that's bad, not much valuation is wrong? investments; facilitate an exit > difficult Re-adjust calculations you can do Experiences lead to new Learning process in Evaluation database behaviours and change Repeat the due diligence you Review strategies every year in place? Database of term sheets intuition started with, adjust on the fly a long session

VC Behaviour Yes US --> huge volumes to fill debts, 1 out of 10 sucessful; Everyone makes errors, good most successful invest in the VCs just make up for it with their trend itself, not just single star investments companies

Valuations right now are too Europe --> state and EU Do most investors fail At the moment they take the high, but coming from private money, cheap money; this to analyse IT startups decision, most are acting rationally Many don't due proper due market bubble, big companies drives overvaluation; 38% of correctly? and with plausible basis diligence way too highly valuated funding was public Yes Yes Most investment opportunities do not give inflation protection & good Lots of liquidity in the market returns due to low interest rates; funds bombard the market with Margins of real estate getting lower money as prices rise Hype on the startup founding Also many former successful side, it's hip for young people Hype combined with lack of people that invest Hype & Risk of alternative investment options Many investment (programs) investments being mis- Unicorns start getting financed by state money, just invested? Also scrutinized more now though N/A shooting it out there

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 76 September 2016

Closing Hype in creating startups

Is there a hype right Institutional investors really now? Yes, bubble good in selecting an investment Bubble; can't be worth 50 bn if no profit was ever generated valuations not justified, too Special rights venture contracts much Exceptions are those that don't If would translate those valuations make profit because they Intangible products, how to you into stock markets, would be much invest so much in expansion, put worth on an idea (examples: lower like Amazon Google, Apple, Uber)

Listed vs. non-listed not 60 bn and not IPO yet, you will Investors expect high return comparable never make profit as investors rates some time in the future If people are willing to pay for it, as public don't want to pay that when the companies start it's ok; believe many are Unicorns Not wrongly priced, but differently price; market won't support this monetizing overvalued, but not all Freelance / Gig economy

AdTech Autonomous driving, Internet of Hot Topics N/A N/A Artificial intelligence Things, Big data, fin tech

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 77 September 2016

Appendix B: Abridged & Anonymised Full Analysis Participants 5-8

Participant 5 Participant 6 Participant 7 Participant 8

Context VC VC VC VC

Experience Finance Finance Tech industry Finance

Education Civil Engineering Business Admin Business Informatics Business Admin Was entrepreneur during Seed VC for student dot-com bubble Principle Investing fund for all startups, early stage capital stages investments Partner at a boutique Startup organization Growth Equity opportunities (no CFO & investment (VC, coaching, seed, VC - rather growth equity, Person & Org Board member of large governmental fund committee acceleration, ...) later stage)

Investment Volumes 200k - 25 mio Euro 10k-100k $ 50k - 150k Euro 20 - 50 mio $

Geographic Focus A specific European country US & Europe European Europe

No specific focus, Tech & Internet (B2B more Industry Focus General tackling big problems B2B Tech companies strongly)

General Young founders, early stage that wouldn't otherwise get funding; Network-driven, based Already clear revenue streams and this way can get bigger on recommendations clear path to profitability in next 4-5 chunk of the company from their network (e.g. if years in high growth environment other investors want (disruptive in market or growing Goal: Get them to the A them in for their market) round, let other expertise) investors take it from Clear path means break even on there Team - serial operating profitability level & entrepreneurs adjacent business models that Try to be at the pulse of Model - scalable show that it can work in steady Find and support most promising early or seed time by investing in Company - Survive state after growth slows down stage teams and companies and help them scale youngest founder independently up to reach series A and B where we also co- generation, rather than Investor - Value Add? Strong Management team, great invest investing in what media Industry - B2B idea and VCs (which are Exit - Already see an exit Investment Strategy older) think is hot stuff channel Excess of unicorns right now

early stage companies getting funded where they shouldn’t, particularly because of the availability of public money and incentive schemes, and fiscal incentives schemes, and structural funding in Europe, and overhype in the US.

when you have very low interest rates, and when Consumer media picking investors, institutional investors, are chasing up on the topic return, there will be a tendency for those investors to allocate capital to higher risk, potentially higher Unclear business return asset classes, what that means is that you models ("I don't really are diverting money from public equities and get it") bonds into alternative investments, such as hedge funds, private equity, real estate, and VC to a Value of some A lot of funding and pressure to certain extent. pressure to deploy that capital companies derived by deploy capital --> leads to high starts. So this bubble, in a big way is fueled by many indirect factors valuations and goes into startups Parallels to Dot-com that cheap money available and increased that don't really tie that will struggle if there was times allocations marginally to VC N/A together well another funding environment Investors pushed companies to go public very early before they were ready

Everybody, also normal people, got caught up in the hype and invested in anything that had anything to do with the internet More due diligent nowadays (work with external advisors, high hurdles Valuations now are a lot to get convinced) saner; investors Markets are much bigger &much more mature became more cautious, Focus on survival of company Differences to Dot- not willing to drive up once funding is turned off, is it com times valuations so much possible, can it survive on its own Careful not to get The earlier you get caught up in buzz and customers the better invest in areas they Lessons taken from don't know Companies should come Dilligence is important due to the Dot-com times from the eco-system, not number of startups around

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 78 September 2016

There are still some go into a market they "pockets" though where have no idea about everybody wants to be in early on and valuations are crazy

Know How

Market Knowledge Importance Rating 5 4 5 4 Without knowledge you can't help the company Can learn quickly about the market 80/20 rule - can dig really deep into also can't make a good Important to evaluate the industry in 2 months judgment call about the actual value the actual opportunity business could achieve But need it to actually get a call for an opportunity, for this you need Market Knowledge Absolutely fundamental In early stage about Also important to help networks Importance Only VCs that can develop quickly deep industry quality of idea, market with your network in Reasoning knowledge in the field size and founder team growing and for exits Don't need to be absolute expert

Own Market Knowledge 4 3 3.5 Software: 5, other Market Knowledge things less clear focus, but more explanation 3-5 (averaged to 4) regional network 3-4

Knowledge of technology/tech trend importance rating 5 4 5 3

If you don't understand you are going to miss out Going in early, need to on companies that will be leading that trend have an understanding Can always get an expert in to get Explanation Intertwined with knowledge of industries an evaluation of the technology Own Knowledge of technology/tech trend rating 4 4 2.5 Call in trusted advisors Own Knowledge of technology/tech Also rely on references reasoning of the founders 2-3 Do extra research in some industries, attend conferences, meet experts - even if not evaluating a company at that moment; to build general expertise being able to act fast Important to be specialized, but keep on reading and listening. We travel a lot, go to events, read Other 50% you get an inbound call, as much, learn from others as much as we can. get presented opportunity, then No single place to find all the trends. A lot of Call in trusted advisors you need to do catch up work, information going around. You need to keep on If he himself is able to research How to assess tech studying. A big part of our work is to keep on Also rely on references pitch it, it's a good trend studying what is happening in the market. of the founders indicator 50/50 - constant work vs. reactive

Evaluation Criteria Late investment, so 2-4 years Ask for 5-year model history of company data (but more as an exercise) Analyse the existing business plan and challenge; those companies Mainly look at money are pre-profitability we look at top- needed till exit and how line potential, assessing this with their ownership might Look at business case top-line advisors be affected by that and business model Profitability --> benchmark with Nobody does The "hard" indicators market peers General Financial Discounted Cash Flow, just to see how attractive Method or model N/A (skipped) too early for that for follow on investors All case by case When later investment, standard stuff: Revenue, customer acquisition

But early investment not Revenue, cross-profit, EBIT- Financial KPIs N/A (skipped) really N/A potential, cash burn rate

How to evaluate business model Customer Lifetime- Value importance rating N/A (skipped) 3.5 5

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 79 September 2016

More about LTV Lock-In and repeat development and LTV use/buy is important as Customer Lifetime- in conjunction with other In B2B space acquisition Value importance KPIs like retention, of customers is costly reason N/A (skipped) acquisition etc. and long N/A

Market Opportunity rating N/A (skipped) 5 4 4.5 Follow on investors look at that

Also help see the founders the bigger Since don't have any picture - can the use numbers in early stage, case be applied this is key elsewhere or extended

Market Opportunity Wouldn't get involved in The more flexible a reasoning N/A (skipped) a too niche market technology, the better Look at other approaches that solve the problem and use Revenue, customer acquisition, Life-time Value this market as a proxy

SAAS - similar: monthly recurring revenue, how Evaluate in context of fast is it growing macro-economic trends over the next decades Cleantech, enterprise IT - very different metrics and milestones Challenge assumptions With commercial advisor How to assess market on market numbers of opportunity Digital & SAAS quite well known metrics founders N/A More important to understand the underlying revenue model and if it needs to Revenue/profit dev be switched - this leads dynamic? N/A (skipped) to different growth rates

Estimate future Asking the network demand? N/A (skipped) ("Friends") Technology is sound, market is big enough, Talk to existing people are good customers

Very different from later If no customers at all - Lack of historic data? stage investing no deal

Competition rating N/A (skipped) 3 5 3.5 Strategy of the Wouldn't invest if you think company depends on competition is better - if they are what the competition and you can't invest in those, then looks like; lots of older not invest at all players that are not innovating --> exciting; Competition is a good Depends on the management Competition very crowded with sign it is an attractive team that can cope with the reasoning N/A (skipped) startups --> not good market competition There is never no competition Desk research and How to evaluate Let the startup do the asking network competition N/A (skipped) research ("Friends") Problems with B2B is less difficult, evaluating future mechanics work always earnings? N/A (skipped) N/A (skipped) more or less the same We have a risk return angle Others do weighted return

Given the high risk in these and there is a high risk that you lose everything, you look for opportunities that have an outsized return

Outsized - for seed: 10xplus , late stage: 4-5x plus Type of risk: Failing to make it to next funding With liquidity preferences, good round terms might even get comfortable with 3-4x because you are the last Bring in technical investor to come in before it stops experts for tech Extensive discussion burning cash, so good downside Evaluating risk N/A (skipped) evaluation process w. founders protection Talking to network ("friends") - opinions

Friends are potential Evaluate exit customers or acquirers scenarios? N/A (skipped) N/A (skipped) often

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 80 September 2016

Non-Financial Criteria

Team must be Customer Growth, customer General Team is much more important than anything else N/A (skipped) impressive retention

R&D Rating 2.5 3 N/A (skipped) N/A (skipped)

Not a separate function R&D Reasoning Only relevant for more established companies at this stage N/A (skipped) N/A (skipped)

How factoring R&D in N/A (skipped) N/A (skipped)

Yes, especially Engagement KPIs? N/A (skipped) stickiness

Need to be able to pitch it himself and for that needs to understand the Make founders talk to business case 100 potential customers Ask industry experts Understand the price point Look at industry trends Understanding and see how product Ask network for Product/Service? would fit in additional input First sale as validation necessary

Ask network to get to a price point

Understanding Check how competition Business Model? N/A (skipped) is doing it

Importance of Founder Team? 5 5 5 5 Knowledge, capacity to be creative and think out of the box; need to adapt fast; not just raw brainpower, capacity to adapt and be open; If things do NOT go well, need to have a team that can manage that; Ability to work with us, listen to us, deep open (was originally 6, but (was originally 6, but that flexible, can react, you trust and Explain importance relationship that was not an option) was not an option) want to work together Conversations w. team, w. references Internal expertise - people that met hundreds of management teams No Plan B, fully committed Track record in Looking for: trust, honesty, highly achieving, completeness ambitious, able to execute and How evaluate Trust is important and deliver; track record in/with Founder Team? Evaluate psychologically trusted references Personal like previous companies Importance of intangible IT assets/capabilities? 1 5 Sales is more important than technical skills, can Just believe in the team but not the Why? N/A (skipped) always buy those in product, that's an issue

How evaluate intangible IT assets? N/A (skipped)

Importance of networks 5 1.5 In B2B this is how you can understand how to solve a problem; you need to come from the That's what investors bring to the Why? N/A (skipped) business table

References, talk to How evaluate? N/A (skipped) clients

Learning Needs to have the right chemistry with the founder team, this is relationship-driven Mainly in the human component: Sees patterns from But want to back this up with facts Evaluating the experience and other perspectives founders, especially if something seems "off" Red flags - e.g. lack of Financials: No gut feel at all; if Intuition, gut feeling, leadership market doesn't exist yet - you really personal Judging if someone will believe personally that the market experiences? be "coachable" If not sure, not doing it develops, then maybe also

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 81 September 2016

Tie everything together More of a financial investor, less into financial models, operating investor then examine all assumptions and if the Rely on outside advisors - hypothesis works consultancies, former executives of through every step similar businesses Also rely on management team Also, factor in human component into strategy Invest into the management team, (are founders willing to you need to be comfortable with How to avoid errors? N/A (skipped) move?) what they tell you Roll it up from milestone back and then come up with valuation based on that

Later stage: typical valuation stage KPIs, How adapt evaluation revenue, etc. - can calculate current discounted models? cash flow N/A (skipped) N/A (skipped) Should understand why other Detach emotionally, let market participants see the What do they do if them be company differently/apply different their valuation is Let professional value - you should still stick to your wrong? N/A (skipped) investors fix it assessment, if it is sound Track valuations over time and Learning process in cross industries & historic place? N/A (skipped) Talk to more friends valuations

VC Behaviour Most investors are followers, they neglect due dilligence Some investors are more bullish, More variability above series A and on seed stage Angels are too cautious some more value-oriented - depends on the type of investor; Angel, fund? Valuations so high that Do most investors fail Are they experienced? Hard to say wether people VCs follow hypes too cannot make significant Lots of capital that needs to be to analyse IT startups are doing the right valuation or not; based on much - "we need to be money; already down deployed and public markets that correctly? promise and growth potential in there" rounds in the US drive up valuations Money goes into not so solid, but hyped companies while great Hype & Risk of startups in an early investments being stage struggle to raise mis-invested? money

Closing Valuations are unjustified and No, not really valuations levels will come down - Yes But media is sometimes hyping certain people Bubble? Hard to predict and companies, and often for the wrong More in the consumer Lot of money invested in unjustified reason - because they business valuations without rooting in the n Is there a hype right managed to raise now? money B2B very conservative "Not many people really understand in a lot of details how these valuation levels come together to create a unicorn "Some unicorns deserved their valuations above valuation. a billion, because they are growing extremely fast and sometimes even profitably. All these valuations in a private round - this is all on paper. It A lot of unicorns that were created in the last 3 doesn't really mean that this is a years and many of them should not have had long-term stable valuation level. such high valuations at that stage. It's sobering to There is also an inherent risk at see that when these companies go public they becoming a unicorn. You are normally trade down. Some individual cases applying a high valuation to your Particularly for some of the more hyped cases in were overhyped as company and as long as you are the world the day will come when they will be founders were good at Lots of money in the not cashflow positive this can acquired or go public and personally I forsee that (just) that: raising market actually be quite hurtful aswell. sky high valuations - particularly above 10 billion - money will be extremely hard to protect. Those are outliers Unicorns attract all Overall I'm sceptical about the Many others in the low range of 1 - 5 bn which I though resources (also overall quantity of unicorn think are also just worth a fraction of that. It's part employees) from not valuations but also the quality of Unicorns of the industry that you have a high failure rate." growing "old" companies unicorn valuations"

Hot Topics N/A N/A

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 82 September 2016

Appendix C: Abridged & Anonymised Full Analysis Participants 9-12

Participant 9 Participant 10 Participant 11 Participant 12

Context VC Angel VC Angel

Experience Tech industry / startups Tech Industry / Startups Tech Industry Tech Industry

Education Economics Computer Science Business Admin Business Admin VC arm of a large company and startup building incubator

Venture developer - find startups in Business Angel for student Person & Org tech sector startups Principal at VC Firm Angel Pack

Investment Volumes 50k - 2 mio; follow up rounds 3k - 10k 200k - 50mio 20k - 200k $

Geographic Focus Worldwide Specific European country Global Global Internet companies, only digitally iOT, urban smart and connected enabled models devices, markets related to operations Industry Focus of parent company Consumer Software

General Stage: Early Stage investor, then invest across life cycle disruptive that in best case already have a little bit traction Category-Defining businesses, help to Better to invest in teams not accelerate growth with capital, know we help find product-market fit and ideas how help scale Only invest if founders also Businesses where there is a value add Investment Strategy invest financially beyond finances Invest in people FOMO - Fear of missing out

"People invest in certain sectors because of FOMO - fear of missing Most models thought out back then out or some kind of herd culture - just turned out to be valid business models following other investors because it sounds like an interesting opportunity. "Most of the models that were thought Or there is a hype generated." Newly founded companies out or started back then - they turned seek investment, they don't out to be valid business models. But Parallels to Dot-com Herd-culture, hype --> Virtual Reality; want to make money in Exits, back then there was just no customer times biggest investment volumes in 2015 but in investment rounds base." N/A No customer base back then Investments that were raised didn't have any fundamental backing; Now everyone is online Market for internet was very small, 1/10th, slow internet Now lower entries to entry for founders, enables to test the waters, "The market was just one tenth of In 1997 was one shot to make gather data; enables more informed what it is now. Internet was very slow. a really big wheel investor decisions This has changed tremendously. We have a totally different market situation Now money is flowing into the Investors became more sophisticated now." company, not the and specialized in certain industries; Differences to Dot-com shareholders; only at (last) knowledge wasn't there back then on times Now >3bn ppl exit can cash out detail questions N/A Timing matters

Be careful when something feels Pets.com was valid, but too early back overvalued then Nobody made the Lessons taken from Not putting the same multiples in calculations; should always We try to catch the right time for a Dot-com times valuations now as back then try to do it company N/A

Know How

Market Knowledge Importance Rating 4 4 4 3 B2B - 5 B2C - 3 (above "4" is average) Without ideas of the industry you could get tricked More important to understand the Only need to know Qualified assumptions & hypothesis business models than the customers, enough to know red Market Knowledge put out by the startup you need some Bring in the network and that because can tell a lot of the dynamics flags and risks; rest the Importance Reasoning knowledge is usually industry knowledge from the data founder must know

Own Market Knowledge 4.5 4 3 2

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 83 September 2016

Market Knowledge explanation 4-5

Knowledge of technology/tech trend importance rating 4.5 2.5 3 4 Changing very rapidly; more important that the software architect/CTO know what they "The more technical the business do model is, the more important"

Check the architecture with don't need to understand in depth, external expert that's why you invest in good teams Explanation 4-5(averaged) 2-3 (averaged) with good knowledge Own Knowledge of technology/tech trend rating 4.5 2 3 4 Own Knowledge of technology/tech reasoning 4-5 Assess the potential Talk to companies and see through impact on consumer Market experts, internal organisation - pattern recognition what new trends behaviour & general life industry knowledge 5-30 years come up improvement Always triggered from people How to assess tech Buy in external experts for specific who approach him, no active Backtrack business models that might Even without data, trend cases search for startups fit intuitively is fine

Evaluation Criteria No internet investor uses DCF models, no data for that

Not a standard model, more multiple based evaluation with reliant on experience companies in the sector with the same business model ideally small calculations are Potential exponential growth sufficient; if it's too Bottom up: sanity check; forecast, General Financial complicated, something might sensitivity analysis if growth is actually Method or model be wrong possible for a good return No Depends on the phase

Seed funding: can only show trends Revenue GROWTH, can be unsustainable at Do projections, models, assumptions start, but must have a to see how market share impacts plan to make the growth Financial KPIs revenue sustainable

How to evaluate business model

Customer Lifetime- Value importance rating 5 4 5 Customer Lifetime- Transactional only in connection with Value importance Mission/Vision: High customer lifetime CAC and scalability reason value; looking for sticky business (fill in later) Transactional: 5

Market Opportunity rating 4.5 2.5 5 Markets are changing fast, competitors are entering fast Market Opportunity Whale-hunters - no little fishs, few big Hard to gather in advance; sometimes reasoning whales create their own market

How to assess market opportunity

Revenue/profit dev Yes, definitely dynamic possible, but Linear; will challenge hockey dynamic? usually exception stick assumptions Expert panels to evaluate future of the market

Estimate future Old revenue streams will vanish, so demand? this is paramount, be very diligent

Founder team - industry knowledge, Valid data on unit economics and general experience, do they scalability is clear, you don't have to understand the dynamics of the have large history market Any market has at least some kind of historic data, and Otherwise all other factors need to be Lack of historic data? every business models too better: blue ocean, exceptional team

Competition rating 5 5 4 You have to know your competition, how they do their can't really predict; always assume If there is no competition, there is a job and are they able to copy great business model always attract Competition reasoning reason for that you competition

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 84 September 2016

Initial analysis by the startup

How to evaluate "There isn't" --> ask them to competition go back researching Problems with evaluating future Only can do short-term, earnings? N/A (skipped) everything longer gambling Look at the different factors that make a great company (market, business Only 1 out of 10 suceeds in our field model, team, unit economics), and size it up If we thought we found 10 whales, we Evaluating risk know only 1 survives Only low sums Multi-factor model Yes, but more for first 2-3 years only (buying shares Yes, but not the goal back for multiple fixed at beginning) Try to find potential acquirers Build long-term business models that no exact science; can't plan for it Sometimes, by looking Evaluate exit can be implemented in the existing Multiples of revenue, not profit at market trends and scenarios? structure for the shares needs

Non-Financial Criteria "Team is the most important, but also most difficult factors to size up for an Outside financial key figures, investment" not many Founder - growing and "a B-Team can screw up an A learning; listening to General Qualitative Feedback opportunity" investors

R&D Rating N/A (skipped) 2 N/A (skipped) N/A (skipped)

Prototyping is not same as R&D Reasoning N/A (skipped) R&D N/A (skipped) N/A (skipped)

How factoring R&D in N/A (skipped) N/A (skipped) N/A (skipped)

Growth, conversion, Engagement KPIs? return users/visitors

The earlier an investment, the more important to understand this as an Shouldn't require big Structured in "knowledge centers", Use it himself, give it to investor yourself and having a consumer behaviour Understanding each of which have expertise for people who need it, hypothesis on how this will play out on change, they are difficult Product/Service? specific area qualitative feedback the market to evaluate Of highest importance for investment; some businesses are great businesses but no good investments

"Some businesses are great Has to understand in every businesses but they are not good detail investments. Because they are too expensive to build or can't grow big Should be viable from Friend of very lean startups, enough or have other dynamics that day one, not just "some Understanding component-based founders; prevent them from exiting at some day when we have Business Model? outsource what is possible 5+ point." users"

Importance of Founder Team? 3.5 5 5 5 With a good business model and team you will find a market

We might add value from ourselves, (was originally 6, but that was Explain importance put somebody into the team not an option) Track record, sector knowledge

Standardized psychological Interaction with the investment team; assessment test setup of the team, how they First get to know the market, then complement each other interview the team Discuss with the team the Want to have the feeling rules for the future company How the investment material is that I would like to be How evaluate Founder Expertise, experiences, approach to to see how they think and prepared - is also a symptom of the part of the startup Team? market interact team myself Importance of intangible IT assets/capabilities? 5 4 Possible to buy more skills in with money (education or people)

Soft Skills: 6

Why? Other Skills 4-5 N/A

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 85 September 2016

Soft skills: see above How evaluate intangible Hard Skills: by marks and The engineer(s) need(s) IT assets? opinions of other teachers N/A to be very strong

Importance of networks 2.5 1 Angel should bring in the networks; young people don't know the important people

In this region it is not easy to get access by yourself to such Why? people N/A

How evaluate? N/A

Learning More than I should As little as possible

Sympathy for the founder is "Given the uncertainty there is always important a large portion of gut feel and belief in there. Sometimes not easy to talk That's something that gets better over about failures then; easier if time, with pattern recognition"; When reading people hardline business and communication approach Experienced investors tend to do Integrity is most Only speak for myself: Definitely part better important of the game, always mix between Market and marketing side it market insight is important to use intuition; "Team is hard to nail down with When evaluating Intuition, gut feeling, hard facts with how to build quantitative measuring, so that's assumptions and personal experiences? Once we amas the company are important where this come in the most" ambitions Involve several people, investment team Evaluate the documents the startup Check the team thoroughly; gave look at small behaviours also Carefully evaluating everything

Independent market analysis and due Are they really able to go Investment committees to catch How to avoid errors? diligence process through hell together overconfidence

How adapt evaluation models? Work with the founders to fix it Help the team to change the path, get back on track What do they do if their Early stage problems are a lot valuation is wrong? but not heavy Help pivot Nothing, too late Improving the evaluation of all the different factors that make up a company success; this is an on-going Regular startup best practices - lean process startup business model canvas, management Investment committee is the safety techniques from Peter Drucker, BCG net; institution that helps the team in matrix assumptions Get better at reading Exchange experiences with people, better at Learning process in MVP thinking, Agile development other investors to get ideas Financial valuation --> mispricing is coaching the team and place? and knowledge not a big factor train leadership

VC Behaviour Some investors are black sheep

Some investors invest in every new idea Everyone makes mistakes

A lot of show around the On average according to Kaufmann Venture Capitalist foundation - VC have not done a good job in the past It is important to find the right 80/20 investor fitting to a startup Some funds make successful Do most investors fail within field of specialized rather than just a well known investments Yes, most don't even to analyse IT startups one or one with the most care about generating correctly? 60% are good 40% are followers money There is some luck involved profit, just about exiting Hype is the wrong word as this would mean we are in a bubble

The startup culture is new entrepreneurial mantras, they became Hype & Risk of fundamental part of economics, investments being mis- invested?

The Next Tech Bubble - Lessons from the Dot-com Crisis for Today's Investors 86 September 2016

Closing Yes, too much money in the market

No interest rates for other investments, stocks are stagnating Housing market is hot, is also bubbly

Everybody wants to invest in startups right now; each Dax No, not necessarily company and mid-sized company has a venture arm looking for the next big thing; Internet sector is clearly pointing towards this is a growth sector the Problem for the founders - next decades think they only need a good Is there a hype right idea and then receive lots of Some decisions in hindsights might be now? money wrong, but mostly rational decisions Don't like the term

Current unicorns are strongly under scrutiny Some like Airbnb are ok, they make sense - market was Necessary breakout success for a existing before, just fund Already have Decacons (?) fragmented; combination w. p2p was great idea; no Some are justified, some are not; can Can't compare it to the dot-com competition say from our own assumptions already bubble because now some companies if a company is overvalued even though they have not the Others not revenue streams that totaly ratify their E.g. Same day, same hour Willing buyers, willing valuations, they are on the way getting deliveries - don't see how this sellers - the market is Unicorns there is such a game changer regulating this

Artificial Intelligence, Hot Topics N/A Artificial intelligence, machine learning Fin-Tech